The microbiome, defined as the complex community of diverse microorganisms that inhabit the human body, along with their area of influence (including interactions, metabolites, and genetic material), has recently gained significant attention due to its profound impact on health and disease. As highlighted by Hou et al. (2022), the microbiota composition is diverse and differs between different sites of the body (e.g., the gastrointestinal system, skin, mouth, oral cavity, respiratory system, or reproductive system). Among these, the gut microbiome plays a crucial role in maintaining homeostasis. However, an imbalance in the microbiota (dysbiosis or loss of homeostasis) can lead to the development of gastrointestinal diseases, such as colorectal cancer (CRC).
Consequently, a significant concern exists about the potential effects of antibiotics and other medications on cancer therapy due to their bidirectional impact on the microbiota composition. This is the goal of this research, in which we are trying to understand if a potential bacteria or bacterial cluster are present or absent in our two groups of colorectal cancer, classified by chemotherapy treatment toxicity.
Between October 2017 and April 2021, we prospectively enrolled 36 adults with histologically confirmed colorectal cancer at the University Hospital Complex of A Coruña (Spain). One faecal sample targeting the 16S rRNA V3–V4 region was collected from each patient before the first chemotherapy cycle; antibiotics and probiotics were prohibited for the preceding four weeks. All patients started a doublet of oxaliplatin (OX) plus 5-fluorouracil (5-FU, FOLFOX-like) and/or radiotherapy.
For this reason, our oncologist designed the toxicity variable following the next criteria: Clinical metadata were extracted from the electronic record and merged with the microbiome profiles. The primary outcome is a dichotomous toxicity target variable: low (\(n=11\)) versus severe (\(n=25\)), defined by CTCAE v5.0 together with a \(\ge 20\,\%\) chemotherapy dose reduction or suspension of any of 5-FU and/or OX. Absence of systemic chemotherapy was the sole exclusion criterion, yielding the present cohort of 36 patients. Detailed coding rules and extended baseline characteristics are provided in Supplementary Table S1 of this paper research.
RQ1: Would there be similarities among the DAA methods outputs applying different microbiome data transformations?
RQ2: Would there be similarities among the DAA methods’ outputs with and without previous prevalence filtering criteria?
RQ3: Could it be possible to identify differential features in terms of their abundance between two toxicity groups? (The groups of study variables were defined under the criteria of oncologists).
Importing the phyloseq object:
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 6082 taxa and 120 samples ]
## sample_data() Sample Data: [ 120 samples by 45 sample variables ]
## tax_table() Taxonomy Table: [ 6082 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 6082 tips and 6004 internal nodes ]
All decontaminat steps were done in the previous steps of the paper (NOTE(1)), so this ps object has no contaminant included. As the derep_ps objects comes from another paper (NOTE (1)), the sample data contains some extra columns.
Loading and Cleaning Data
Since some patients had replicated samples, we aggregated their measurements using the median.
Transform relevant variables (toxicity variable and gender) into factors
The first step is to evaluate the number or retained taxa applying different prevalent values (it should be present in more than 1 person). For that, we should take in mind that we have a total of 36 patients. Then, if we extract the 1’% of 100 individuals, and adjusted it to our N, the prevalence would be associated to 3–4 individuals (3.6 individuals exactly). Trying different prevalence values:
Prevalence:5% (1045 taxa)
After testing multiple prevalence thresholds, a 5% cutoff was chosen to maintain a meaningful number of taxa (1,045) while ensuring their presence in roughly two out of 36 patients.
DataFrame summary related a selection of relevant variables
The goal of this step is perform a pre-understanding of our data and the relevant variables that could be use in further steps.
Genus Level Glom Taxa (unfiltered ps) Glom taxa to Genus level using our phyloseq object (tox_ps_final)
Plot: Toxicity associated to gender distribution
NOTE: This code was adapted from Conde-Perez et al. (2024) paper, referenced in NOTES section.
A quick by to our dataset, to see the distribution by sex and age range in our two-toxicity classes. In the following plot the subject toxicity associated density based on sex and age can be seen per Toxicity variable (TOX_SEVERA):
This plot
compares the age distribution by toxicity group and by sex. In the
Low_Tox panel, the sample is markedly male-skewed (2 females vs 9
males). Ages in this group are mostly older: one of the two females
falls in the 50–60 range, whereas nearly all remaining observations lie
between 60 and 90 years. In the Severe_Tox panel, the sex ratio is more
balanced (11 females vs 14 males), and both sexes skew older, with most
individuals aged ≥70 years. These counts and age ranges refer to the
individuals shown in the plot.
After reviewing various methodological options, we decided to perform a GPower analysis. The GPower results supported the relevance of our findings within the cohort, highlighting that the microbiome’s significant differences observed in one of the two toxicity groups are likely due to the presence of strong abundance shifts that are detectable even with a relatively small sample size (36 patients).
Additional tools and R packages, such as micropower, were also considered. However, micropower could not be applied in our case due to its reliance on simulated data rather than empirical input.
NOTE: micropower is a specialized R package designed to estimate statistical power and sample size in microbiome studies. It helps researchers determine whether their cohort size is sufficient to detect differences in the relative abundances of bacterial taxa.
As the next step, we performed a benchmarking of several differential abundance analysis (DAA) methods to identify microbial taxa significantly associated with one of the two study groups. Patients were categorized based on the severity of chemotherapy-related toxicity, as assessed by oncologists: those who experienced mild or manageable toxicity were assigned to the low toxicity group, while those who experienced severe toxicity and/or required dose reduction or discontinuation of one of the two treatment regimens were assigned to the severe toxicity group. More information is available in the paper.
Given the variability in statistical assumptions and performance across DAA methods, we implemented an ensemble consensus approach to increase robustness and reduce method-specific biases. This approach involved comparing the outputs of multiple DAA methods and identifying consistently detected taxa (i.e. those that appear as significantly differential abundant across all methods). These overlapping results were considered the most reliable candidates for biologically meaningful group-specific microbial signatures.
The main objective of this ensemble strategy was to highlight consensus taxa and assess the reproducibility of findings across different analytical frameworks, thereby enhancing the overall validity of our results.
Methods and Strategy
In this section, we apply a diverse set of differential abundance analysis (DAA) methods, each based on different statistical assumptions, data transformations, and normalization strategies. Despite their methodological differences, all share a common goal: to identify taxa that show significant differences in abundance based on a grouping variable.
We evaluate each method under multiple conditions:
A distinctive aspect of our study is the clinical definition of the comparison groups. Unlike conventional case-control designs, our study focuses on two groups of CRC patients defined by chemotherapy-induced toxicity:
This grouping was based on clinical evaluations and treatment modifications, and reflects a real-world, treatment-driven classification. The grouping variable used for DAA is therefore both clinically meaningful and directly relevant to the outcome of interest.
In this unfiltered approach, we are using the non-filter by prevalence phyloseq object named: tox_ps_final_genus. For biomarker discover purpose in our clinical prediction, we aggregated to genus level.
We first applied the ANCOM-BC method
(ancombc function) to identify differentially abundant taxa
between the two toxicity groups. For this initial analysis, we used the
following settings:
prv_cut = 0)p_adj_method = "none")qval_cut = 0.05)alpha = 0.05target_variables. Only used in the covariates section.The analysis was performed at the ancom_target taxonomic
level using parallel processing with 8 cores
(n_cl = 8).
We used the argument conserve = TRUE to apply conservative
variance estimates.
NOTE: As we want to no aggregate due previous aggregation to genus level, then ANCOM-BC target_level parameter should be set at “ASV”. See code for further details.
NOTE (2): The ancombc global function code was adapted from the paper: 16S, Conde-Pérez et al (2024). Reference in NOTE (1).
ANCOM-BC No Prevalence-NoBH Plot
The results provide an initial, unadjusted list of candidate taxa associated with toxicity status, to be compared with other DAA methods in subsequent steps.
We then applied the ANCOM-BC method
(ancombc function) to identify differentially abundant taxa
between the two toxicity groups, this time incorporating false discovery
rate (FDR) control via Benjamini–Hochberg (BH)
correction. The parameters used were:
prv_cut = 0)p_adj_method = "BH")alpha = 0.05target_variablesThe analysis was conducted at the ancom_target taxonomic
level using 8 processing cores (n_cl = 8) and conservative
variance estimation (conserve = TRUE).
NOTE: As we want to no aggregate due previous aggregation to genus level, then ANCOM-BC target_level parameter should be set at “ASV”. See code for further details. So, we also run the main function on each taxonomic level with a prevalence cut of 0:
ANCOM-BC No Prevalence-BH Plot
These results reflect a more stringent significance threshold compared to the unadjusted analysis, allowing us to identify taxa that remain robustly associated with toxicity group after correcting for multiple comparisons.
ALDEx2 (ANOVA-Like Differential Expression) is a method designed to identify differences in feature abundance between groups in compositional data, such as those generated by microbiome sequencing (e.g., 16S rRNA or metagenomics). It works by generating multiple Monte Carlo instances from a Dirichlet distribution to account for technical variability, then applying a centered log-ratio (CLR) transformation to the data.
Statistical tests (e.g., Welch’s t-test or Wilcoxon) are performed on these transformed values to detect differential abundance. In our case, as we only have two groups to evaluate, a t-test is performed. This statistical approach returns a p-value (Welch) information (we.ep) and a q-value or correction over p-value through BH (we.eBH). These p-values and q-values come from the Monte Carlo estimation in CLR transformed abundance values. Note*
ALDEx2 also estimates effect size (log-fold change) and includes adjusted p-values (e.g., via Benjamini-Hochberg correction) for multiple testing. Its main strength lies in properly addressing the compositional nature of relative abundance data, reducing the risk of false positives compared to traditional methods.
Note*: In case you have more than two groups to evaluate, then Wilcoxon (kw) test should be tested.
## [1] TRUE
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "Filter alpha 0.05"
## corrected
## uncorrected FALSE
## FALSE 292
## TRUE 5
## [1] "Filter alpha 0.1"
## corrected
## uncorrected FALSE
## FALSE 286
## TRUE 11
Plot: ALDEx2 No Prevalence-NoBH Plot
## ⚠️ No features with p < 0.05 y q < 0.05. So, no plot would be generated.
Filtering using a pval of 0.1 to see the retained at 0.05 and 0.01 pvalues.
In the case of ALDEx2, no features returned q values lower than 0.1 or 0.05 (filtering criteria).
## [1] TRUE
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "Filter alpha 0.05"
## corrected
## uncorrected FALSE
## FALSE 3016
## TRUE 6
## [1] "Filter alpha 0.1"
## corrected
## uncorrected FALSE
## FALSE 3010
## TRUE 12
Plot(ASV): ALDEx2 No Prevalence-NoBH
## ⚠️ No features with p < 0.05 y q < 0.05. So, no plot would be generated.
Filtering using a pval of 0.1 to see the retained at 0.05 and 0.01 pvalues.
In accordance with DESeq2 authors’ recommendations, we applied a pre-filtering step to remove features with very low counts, particularly given our relatively small sample size (i.e., ≤ 10 samples in the smallest group). This filtering helps reduce memory usage, improves computational efficiency, and enhances the interpretability of downstream analyses such as PCA and dispersion plots by removing uninformative features.
We employed the Wald test for differential expression analysis, as
our experimental design involves a binary comparison (two conditions in
the TOX_SEVERA variable). The Likelihood Ratio Test (LRT)
is more appropriate for more complex models or multi-factor designs, and
thus was not used here.
Finally, we compared raw p-values with those adjusted using the Benjamini–Hochberg (BH) method for False Discovery Rate (FDR) control. This comparison allows us to assess the number of features filtered out by the multiple testing correction process, and to better understand the trade-off between sensitivity and specificity in identifying significant genes or taxa.
features with pvalue (raw) < 0.05 and < 0.1 to see the difference
features with pvalue (raw) < 0.05 and < 0.1 to see the difference
LEfSe (Linear Discriminant Analysis Effect Size) is a statistical method designed to identify features (e.g., taxa) that are both statistically significant and biologically relevant across predefined groups. It combines non-parametric Kruskal–Wallis and Wilcoxon rank-sum tests with Linear Discriminant Analysis (LDA) to estimate the effect size of each differentially abundant feature.
In this analysis, we applied LEfSe using both CPM-transformed counts and relative abundances, as recommended in the literature. Since LEfSe does not implement any multiple testing correction by default, Benjamini–Hochberg (BH) correction was applied manually to the resulting p-values to allow fair comparison with other differential abundance analysis (DAA) methods.
We applied the LEfSe method to identify differential abundant taxa between the two toxicity groups, using CPM (counts per million) normalized data. The parameters were settled to 0.05 in Kruskal-Wallis and Wilcoxon tests.
The class variable used to define the groups was toxicity level (low vs. severe). LEfSe was then applied to detect taxa that are not only statistically significant, but also biologically consistent across the groups, using a non-parametric approach followed by linear discriminant analysis (LDA).
NOTE: To ensure numerical stability during the LEfSe analysis, a small pseudocount (1e-06) was added to all abundance values prior to normalization. This step is essential because LEfSe applies logarithmic transformations during its processing pipeline (e.g., after CPM or relative abundance normalization), and zero values can lead to undefined or unstable results when log-transformed. The pseudocount is small enough to have negligible impact on relative differences but prevents errors and maintains consistency across features during differential abundance testing and LDA effect size estimation.
We also applied the LEfSe method to identify differential abundant taxa between the two toxicity groups, using Relative Abundances normalized data. The parameters were settled to 0.05 in Kruskal-Wallis and Wilcoxon tests.
The class variable used to define the groups was toxicity level (low vs. severe). LEfSe was then applied to detect taxa that are not only statistically significant, but also biologically consistent across the groups, using a non-parametric approach followed by linear discriminant analysis (LDA).
NOTE: To ensure numerical stability during the LEfSe analysis, a small pseudocount (1e-06) was added to all abundance values prior to normalization. This step is essential because LEfSe applies logarithmic transformations during its processing pipeline (e.g., after CPM or relative abundance normalization), and zero values can lead to undefined or unstable results when log-transformed. The pseudocount is small enough to have negligible impact on relative differences but prevents errors and maintains consistency across features during differential abundance testing and LDA effect size estimation.
LinDA (Linear Models for Differential Abundance) is a statistical method designed to detect differentially abundant microbial taxa between experimental groups in compositional microbiome data. It fits a linear model for each taxon, adjusting for compositionality and potential covariates (e.g., confounders like age or sex).
Output Structure:
LINDA returns a list of data frames, each corresponding to a tested
variable (TOX_SEVERA in our case). The reference group of the tested
variable is the FIRST one that appears when you launch the command
levels(sample_data(tox_ps_final_genus)$TOX_SEVERA). Each
row represents a taxon and includes:
log2FoldChange: The estimated effect size (positive values indicate higher abundance in the reference group, negative values in the compared group).
padj: Adjusted p-value using methods like Benjamini-Hochberg.
reject: Logical indicator (TRUE/FALSE) denoting whether the taxon is significantly differentially abundant (based on padj threshold).
Additional fields: baseMean, standard error (lfcSE), test statistic, and degrees of freedom.
Interpretation:
Direction of Change:
log2FoldChange > 0: More abundant in the reference group (low in our case).
log2FoldChange < 0: More abundant in the comparison group.
Magnitude: Larger absolute values of log2FoldChange suggest stronger differential abundance.
Confounders: When present, LINDA provides separate outputs per covariate, allowing assessment of each variable’s effect independently.
LINDA is particularly suited for microbiome data due to its compositional adjustments and flexible handling of multiple covariates. More information able in the official tutorial: tutorial https://github.com/zhouhj1994/LinDA
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
ZicoSeq is a linear model– and permutation-based framework for differential abundance analysis designed for zero-inflated, compositional sequencing data. It accepts raw count or proportion data, along with associated metadata (e.g., grouping and covariates), and is particularly robust to common microbiome data challenges such as sparsity, outliers, and variability in sampling depth.
The method begins by filtering low-abundance and low-prevalence features. It then applies winsorization to limit the influence of outliers, and—when using count data—may incorporate Bayesian smoothing via a beta-mixture model to account for sampling variability and zero inflation.
Normalization is conducted using a reference-based iterative procedure, which identifies a stable set of features by excluding highly variable taxa. Differential abundance is then assessed using a linear modeling approach over multiple data transformations (e.g., log, square-root), enabling flexibility in capturing different types of feature–group relationships.
Statistical significance is determined using a permutation-based omnibus test, which preserves the correlation structure of the data. ZicoSeq reports both raw and adjusted p-values (FDR, and optionally FWER), along with effect size measures such as R² and signed test statistics, supporting robust biomarker discovery in compositional datasets.
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 144 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
In the case of ZicoSeq, R² values were used as proxies for effect sizes (logFC) for the purpose of comparison across methods.
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 224 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's fill values.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
Here, we are using the phyloseq filtered by prevalence object, named physeq_filtered. We also aggregated to genus level for biomarker discover purpose and named it physeq_filtered_genus:
NOTE: As we want to no aggregate due previous aggregation to genus level, then ANCOM-BC target_level parameter should be set at “ASV”. See code for further details.
Filtering by qval
Final ANCOM-BC df results:
ANCOM-BC package are used in differential abundance analysis.
Different target_level have been used.
Default p-value correction Holm changed by Benjamini-Hochberg.
NOTE: As we want to no aggregate due previous aggregation to genus level, then ANCOM-BC target_level parameter should be set at “ASV”. See code for further details.
ALDEx2 (ANOVA-Like Differential Expression) is a method designed to identify differences in feature abundance between groups in compositional data, such as those generated by microbiome sequencing (e.g., 16S rRNA or metagenomics). It works by generating multiple Monte Carlo instances from a Dirichlet distribution to account for technical variability, then applying a centered log-ratio (CLR) transformation to the data. Statistical tests (e.g., Welch’s t-test or Wilcoxon) are performed on these transformed values to detect differential abundance. ALDEx2 also estimates effect size (log-fold change) and includes adjusted p-values (e.g., via Benjamini-Hochberg correction) for multiple testing. Its main strength lies in properly addressing the compositional nature of relative abundance data, reducing the risk of false positives compared to traditional methods.
## [1] TRUE
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "Filter alpha 0.05"
## corrected
## uncorrected FALSE
## FALSE 205
## TRUE 7
## [1] "Filter alpha 0.1"
## corrected
## uncorrected FALSE
## FALSE 202
## TRUE 10
## ⚠️ No features with p < 0.05 y q < 0.05. So, no plot would be generated.
## [1] TRUE
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "Filter alpha 0.05"
## corrected
## uncorrected FALSE
## FALSE 1040
## TRUE 5
## [1] "Filter alpha 0.1"
## corrected
## uncorrected FALSE
## FALSE 1034
## TRUE 11
## ⚠️ No features with p < 0.05 y q < 0.05. So, no plot would be generated.
As previously described in the analysis without prevalence filtering, DESeq2 models were applied following the authors’ recommendations for pre-filtering low-count features, particularly in cases with small sample sizes (i.e., ≤ 10 samples in the smallest group). This filtering step reduces the size of the dataset, improves computational efficiency, and enhances the clarity of downstream analyses such as PCA and dispersion plots by removing features with minimal biological signal.
In the current approach, we additionally applied a prevalence filter to retain only those features that are present in a minimum proportion of samples across the dataset. This step aims to further reduce noise and focus the analysis on features that are consistently detected, thereby potentially improving the robustness and interpretability of differential abundance results.
##
## non-significant significant
## 82 1
Filtering data by pvalue (0.05) conditions
## # A tibble: 5 × 3
## Genus max_lfc n
## <chr> <dbl> <int>
## 1 g__Lachnospiraceae_NK4A136_group 2.36 1
## 2 g__Fusicatenibacter 2.30 1
## 3 g__Lachnospira 2.19 1
## 4 g__[Ruminococcus]_torques_group -1.07 1
## 5 g__Butyricicoccus -1.82 1
## baseMean log2FoldChange lfcSE stat
## da8b26f82eb70e299518e149ae85f3d9 97.87732 -1.821848 0.7594865 -2.398789
## e655845f5f4ce1633524c0c9a0b15927 89.36681 2.192742 1.0341139 2.120407
## 93b58b0ba0d326e9c8d1a81f8672c16a 393.69020 -1.071669 0.5396934 -1.985700
## 707940842caa2afe60491008e04a8173 287.26363 2.357575 0.6388942 3.690087
## 9df251784dde31e05f02b2ee1029d71c 153.34220 2.304067 0.8033413 2.868105
## pvalue padj
## da8b26f82eb70e299518e149ae85f3d9 0.0164493961 0.45509996
## e655845f5f4ce1633524c0c9a0b15927 0.0339717478 0.70491377
## 93b58b0ba0d326e9c8d1a81f8672c16a 0.0470665962 0.73254510
## 707940842caa2afe60491008e04a8173 0.0002241775 0.01860673
## 9df251784dde31e05f02b2ee1029d71c 0.0041293849 0.17136947
## Genus
## da8b26f82eb70e299518e149ae85f3d9 g__Butyricicoccus
## e655845f5f4ce1633524c0c9a0b15927 g__Lachnospira
## 93b58b0ba0d326e9c8d1a81f8672c16a g__[Ruminococcus]_torques_group
## 707940842caa2afe60491008e04a8173 g__Lachnospiraceae_NK4A136_group
## 9df251784dde31e05f02b2ee1029d71c g__Fusicatenibacter
Final DESeq2 results:
##
## non-significant significant
## 115 1
## # A tibble: 6 × 3
## Genus max_lfc n
## <chr> <dbl> <int>
## 1 g__Lachnospira 7.62 1
## 2 g__Streptococcus 3.25 1
## 3 g__Bacteroides 2.93 1
## 4 g__Lachnospiraceae_NK4A136_group 2.68 1
## 5 g__Fusicatenibacter 2.33 1
## 6 g__Blautia 1.65 1
## baseMean log2FoldChange lfcSE stat
## 9df251784dde31e05f02b2ee1029d71c 153.4869 2.325699 0.8395547 2.770159
## pvalue padj Genus
## 9df251784dde31e05f02b2ee1029d71c 0.005602901 0.3249683 g__Fusicatenibacter
Final DESeq2 results:
This analysis follows the same statistical approach as the previous LEfSe section, but includes an additional prevalence filtering step, retaining only features present in a minimum proportion of samples. This aims to reduce the influence of rare, low-information features and improve robustness in identifying biologically meaningful differences between groups.
As before, both CPM and relative abundance transformations were used, and BH-adjusted p-values were manually calculated to enable direct comparison with other methods that apply multiple testing correction.
NOTE: To ensure numerical stability during the LEfSe analysis, a small pseudocount (1e-06) was added to all abundance values prior to normalization. This step is essential because LEfSe applies logarithmic transformations during its processing pipeline (e.g., after CPM or relative abundance normalization), and zero values can lead to undefined or unstable results when log-transformed. The pseudocount is small enough to have negligible impact on relative differences but prevents errors and maintains consistency across features during differential abundance testing and LDA effect size estimation.
NOTE: To ensure numerical stability during the LEfSe analysis, a small pseudocount (1e-06) was added to all abundance values prior to normalization. This step is essential because LEfSe applies logarithmic transformations during its processing pipeline (e.g., after CPM or relative abundance normalization), and zero values can lead to undefined or unstable results when log-transformed. The pseudocount is small enough to have negligible impact on relative differences but prevents errors and maintains consistency across features during differential abundance testing and LDA effect size estimation.
## [1] "BH correction processing..."
## [1] "BH correction processing..."
As described in the previous section, LinDA applies a linear modeling approach to compositional microbiome data using CLR transformation and permutation-based inference. In this analysis, we additionally applied a prevalence filter prior to modeling, retaining only features present in a minimum proportion of samples. This step aims to reduce noise from rare features and focus the differential abundance analysis on more consistently detected taxa.
As before, both raw and Benjamini–Hochberg (BH) adjusted p-values were calculated to assess the effect of multiple testing correction on feature selection.
## Pseudo-count approach is used.
## $plot.lfc
## $plot.lfc[[1]]
##
##
## $plot.volcano
## $plot.volcano[[1]]
Without BH correction
With BH correction As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## Pseudo-count approach is used.
## $plot.lfc
## $plot.lfc[[1]]
##
##
## $plot.volcano
## $plot.volcano[[1]]
Without BH correction
With BH correction As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
As previously described, ZicoSeq is a linear model– and permutation-based method designed for zero-inflated compositional data. In this section, we applied an additional prevalence filtering step prior to analysis, retaining only features detected in a minimum proportion of samples. This filtering helps reduce the influence of rare features and improves the stability of the reference-based normalization and permutation testing procedures.
Raw and permutation-adjusted p-values (FDR) were again used to evaluate the impact of multiple testing correction on the detection of differentially abundant features.
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 128 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 224 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's fill values.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
In this section we are detecting and visualizing the bacteria that are common between some (or all) the methods, as well as the unique biomarker detected by each method + approach.
## $res_aldex2_noprev_ASV_noBH_df
## pval qval wi.ep wi.eBH
## 15e05255b2aa8ee3524ca61eb207bb18 0.081306247 1.0000000 0.3186740038 1.0000000
## 44cae38ace30f54ee81cc5c5b2ce47eb 0.083697892 0.9773039 0.0474647663 0.9897809
## 4d6fe682a4dd9aad8decfab830a193e0 0.091213372 0.9993405 0.0812125488 1.0000000
## 8f3c8cb8544640becafb5777eb7a5858 0.098661800 0.9922256 0.0709048977 0.9895994
## 582fae36f33acd1efdcaf7cacf00ef0a 0.050969056 1.0000000 0.0485136981 1.0000000
## f664a73e7de6e00ff70e013369499cbd 0.021406655 0.9883441 0.0514005485 0.9952138
## af2150fef76b295092cd60a788cbe500 0.047749749 1.0000000 0.0035463241 0.9893937
## 25496a64517e79fae616627d6927854e 0.063076660 1.0000000 0.0566321001 0.9904108
## 25af29e1b2d121f8aae468d270d75518 0.028208053 1.0000000 0.0393166041 0.9969398
## cc465b18f37cb7e609cff5ba5ed2bffe 0.046345362 0.9809461 0.0323832465 0.9884264
## 9df251784dde31e05f02b2ee1029d71c 0.004430293 1.0000000 0.0008328149 0.8833835
## be1fe9bfe424ad27f568d8e2d7b42c19 0.046123476 1.0000000 0.0202594433 0.9968492
## rab.all rab.win.0 rab.win.1 logFC
## 15e05255b2aa8ee3524ca61eb207bb18 10.618443 10.7822993 10.5448272 -0.5528752
## 44cae38ace30f54ee81cc5c5b2ce47eb 1.843180 7.1178326 1.0823890 -3.9416592
## 4d6fe682a4dd9aad8decfab830a193e0 8.621118 9.9432678 8.3560278 -2.0688815
## 8f3c8cb8544640becafb5777eb7a5858 1.494705 6.7436834 0.9106689 -3.9405175
## 582fae36f33acd1efdcaf7cacf00ef0a 6.369136 1.1387618 7.2385531 4.3958509
## f664a73e7de6e00ff70e013369499cbd 1.653785 0.1804158 3.2609676 4.6571910
## af2150fef76b295092cd60a788cbe500 8.180591 6.1875508 8.5973346 2.6136809
## 25496a64517e79fae616627d6927854e 2.114770 0.4038674 4.3913562 3.7245176
## 25af29e1b2d121f8aae468d270d75518 5.421492 0.7303390 8.6733518 6.4149225
## cc465b18f37cb7e609cff5ba5ed2bffe 1.644493 7.0821186 0.8433434 -4.9008214
## 9df251784dde31e05f02b2ee1029d71c 8.302308 2.8409041 8.6772412 5.6695325
## be1fe9bfe424ad27f568d8e2d7b42c19 7.075798 2.1943857 7.7086057 3.8119329
## diff.win effect effect.low effect.high
## 15e05255b2aa8ee3524ca61eb207bb18 2.378879 -0.1704568 -10.3155817 2.684609
## 44cae38ace30f54ee81cc5c5b2ce47eb 6.405141 -0.5539998 -7.9462601 2.337585
## 4d6fe682a4dd9aad8decfab830a193e0 5.514116 -0.3273025 -7.4253697 3.409693
## 8f3c8cb8544640becafb5777eb7a5858 7.260082 -0.5170380 -6.4659419 2.598227
## 582fae36f33acd1efdcaf7cacf00ef0a 6.951487 0.6009216 -2.3392843 8.256984
## f664a73e7de6e00ff70e013369499cbd 6.238730 0.6496785 -2.0888978 6.012369
## af2150fef76b295092cd60a788cbe500 4.647590 0.5606651 -2.4723960 11.856755
## 25496a64517e79fae616627d6927854e 5.376925 0.6091681 -2.2631984 6.034096
## 25af29e1b2d121f8aae468d270d75518 8.393566 0.6784476 -2.8121496 7.596141
## cc465b18f37cb7e609cff5ba5ed2bffe 7.224294 -0.6078109 -7.1714325 2.091439
## 9df251784dde31e05f02b2ee1029d71c 4.956521 0.9096101 -0.8369337 10.392921
## be1fe9bfe424ad27f568d8e2d7b42c19 6.791876 0.5509743 -2.2798787 6.019764
## overlap bacteria
## 15e05255b2aa8ee3524ca61eb207bb18 0.3929078 g__Collinsella
## 44cae38ace30f54ee81cc5c5b2ce47eb 0.2780142 g__Alistipes
## 4d6fe682a4dd9aad8decfab830a193e0 0.3205674 g__Parabacteroides
## 8f3c8cb8544640becafb5777eb7a5858 0.2911932 g__Parvimonas
## 582fae36f33acd1efdcaf7cacf00ef0a 0.2769887 g__[Eubacterium]_ventriosum_group
## f664a73e7de6e00ff70e013369499cbd 0.2581561 g__Lachnospira
## af2150fef76b295092cd60a788cbe500 0.2113476 g__Blautia
## 25496a64517e79fae616627d6927854e 0.2514205 f__Lachnospiraceae_NA
## 25af29e1b2d121f8aae468d270d75518 0.2567376 g__Roseburia
## cc465b18f37cb7e609cff5ba5ed2bffe 0.2642046 g__Negativibacillus
## 9df251784dde31e05f02b2ee1029d71c 0.1633524 g__Fusicatenibacter
## be1fe9bfe424ad27f568d8e2d7b42c19 0.2471591 g__Lachnospiraceae_NK4A136_group
## enrich_group direction orientation qval.txt
## 15e05255b2aa8ee3524ca61eb207bb18 0 Negative LFC -1 -
## 44cae38ace30f54ee81cc5c5b2ce47eb 0 Negative LFC -1 -
## 4d6fe682a4dd9aad8decfab830a193e0 0 Negative LFC -1 -
## 8f3c8cb8544640becafb5777eb7a5858 0 Negative LFC -1 -
## 582fae36f33acd1efdcaf7cacf00ef0a 1 Positive LFC 1 -
## f664a73e7de6e00ff70e013369499cbd 1 Positive LFC 1 -
## af2150fef76b295092cd60a788cbe500 1 Positive LFC 1 -
## 25496a64517e79fae616627d6927854e 1 Positive LFC 1 -
## 25af29e1b2d121f8aae468d270d75518 1 Positive LFC 1 -
## cc465b18f37cb7e609cff5ba5ed2bffe 0 Negative LFC -1 -
## 9df251784dde31e05f02b2ee1029d71c 1 Positive LFC 1 -
## be1fe9bfe424ad27f568d8e2d7b42c19 1 Positive LFC 1 -
## prev BH herramienta
## 15e05255b2aa8ee3524ca61eb207bb18 no no ALDEX2
## 44cae38ace30f54ee81cc5c5b2ce47eb no no ALDEX2
## 4d6fe682a4dd9aad8decfab830a193e0 no no ALDEX2
## 8f3c8cb8544640becafb5777eb7a5858 no no ALDEX2
## 582fae36f33acd1efdcaf7cacf00ef0a no no ALDEX2
## f664a73e7de6e00ff70e013369499cbd no no ALDEX2
## af2150fef76b295092cd60a788cbe500 no no ALDEX2
## 25496a64517e79fae616627d6927854e no no ALDEX2
## 25af29e1b2d121f8aae468d270d75518 no no ALDEX2
## cc465b18f37cb7e609cff5ba5ed2bffe no no ALDEX2
## 9df251784dde31e05f02b2ee1029d71c no no ALDEX2
## be1fe9bfe424ad27f568d8e2d7b42c19 no no ALDEX2
##
## $res_aldex2_noprev_noBH_df
## pval qval wi.ep wi.eBH
## b0553a7cf3e72573824b0dacf2747ae5 0.007161728 0.5958931 0.013333459 0.7732358
## 1c441acb05af7d8fee64aaf52bf3d223 0.061945731 0.7231110 0.026177356 0.7533942
## 8f3c8cb8544640becafb5777eb7a5858 0.047103718 0.7607209 0.017084796 0.7077159
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 0.092539465 0.8758061 0.075854346 0.8645053
## 114d0aefa07e7ab7836011f44281b737 0.092350848 0.8284039 0.053541246 0.8288619
## 582fae36f33acd1efdcaf7cacf00ef0a 0.016701328 0.9773422 0.009813534 0.7933836
## e655845f5f4ce1633524c0c9a0b15927 0.059441346 0.9988693 0.020032285 0.9080566
## 93b58b0ba0d326e9c8d1a81f8672c16a 0.050689141 0.9764837 0.032680282 0.9230317
## 707940842caa2afe60491008e04a8173 0.055272522 1.0000000 0.025753267 0.9528603
## d16a4faef202f2f76497bdd5a6f454b7 0.049264558 0.6690419 0.020407300 0.7942940
## 9df251784dde31e05f02b2ee1029d71c 0.005184355 0.9050780 0.001301196 0.3453439
## rab.all rab.win.0 rab.win.1 logFC
## b0553a7cf3e72573824b0dacf2747ae5 5.9670568 7.0644208 5.4480364 -1.310869
## 1c441acb05af7d8fee64aaf52bf3d223 -0.6284864 2.7517308 -1.4363635 -3.726530
## 8f3c8cb8544640becafb5777eb7a5858 0.4496205 5.0066903 -0.5332208 -4.492224
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 -0.7502486 3.1082902 -1.3761178 -4.095487
## 114d0aefa07e7ab7836011f44281b737 -0.4186547 3.2534734 -1.1180664 -3.755543
## 582fae36f33acd1efdcaf7cacf00ef0a 5.3371801 1.1157413 6.2603148 3.775015
## e655845f5f4ce1633524c0c9a0b15927 5.2218610 2.7266055 5.8314296 3.135938
## 93b58b0ba0d326e9c8d1a81f8672c16a 8.4697640 9.0942406 8.2406981 -1.243068
## 707940842caa2afe60491008e04a8173 7.2110695 5.6607529 7.7782432 2.202868
## d16a4faef202f2f76497bdd5a6f454b7 1.8246068 3.3260163 0.5232515 -2.799724
## 9df251784dde31e05f02b2ee1029d71c 6.1513335 0.8069237 6.5500678 5.624240
## diff.win effect effect.low effect.high
## b0553a7cf3e72573824b0dacf2747ae5 1.995690 -0.5055607 -10.6558175 1.538845
## 1c441acb05af7d8fee64aaf52bf3d223 5.638639 -0.5839081 -5.9911102 2.859307
## 8f3c8cb8544640becafb5777eb7a5858 6.460318 -0.6829475 -5.4795421 2.068761
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 7.177791 -0.5436808 -5.7309843 1.940185
## 114d0aefa07e7ab7836011f44281b737 5.947602 -0.5278794 -5.7334893 2.115891
## 582fae36f33acd1efdcaf7cacf00ef0a 5.290303 0.6652220 -1.7773935 9.870818
## e655845f5f4ce1633524c0c9a0b15927 5.985813 0.4862400 -2.6690891 7.035152
## 93b58b0ba0d326e9c8d1a81f8672c16a 2.477538 -0.4659539 -6.9315649 2.852879
## 707940842caa2afe60491008e04a8173 3.999880 0.4333537 -1.9164662 9.992439
## d16a4faef202f2f76497bdd5a6f454b7 4.192407 -0.5967869 -8.2312674 2.189089
## 9df251784dde31e05f02b2ee1029d71c 5.101456 0.8958155 -0.8476929 11.615676
## overlap bacteria
## b0553a7cf3e72573824b0dacf2747ae5 0.2542614 g__Family_XIII_AD3011_group
## 1c441acb05af7d8fee64aaf52bf3d223 0.2482270 g__[Eubacterium]_nodatum_group
## 8f3c8cb8544640becafb5777eb7a5858 0.2397164 g__Parvimonas
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 0.2780142 g__Peptostreptococcus
## 114d0aefa07e7ab7836011f44281b737 0.2784091 g__Solobacterium
## 582fae36f33acd1efdcaf7cacf00ef0a 0.2241135 g__[Eubacterium]_ventriosum_group
## e655845f5f4ce1633524c0c9a0b15927 0.2556819 g__Lachnospira
## 93b58b0ba0d326e9c8d1a81f8672c16a 0.2751774 g__[Ruminococcus]_torques_group
## 707940842caa2afe60491008e04a8173 0.2627841 g__Lachnospiraceae_NK4A136_group
## d16a4faef202f2f76497bdd5a6f454b7 0.2368795 o__Oscillospirales_NA
## 9df251784dde31e05f02b2ee1029d71c 0.1730497 g__Fusicatenibacter
## enrich_group direction orientation qval.txt
## b0553a7cf3e72573824b0dacf2747ae5 0 Negative LFC -1 -
## 1c441acb05af7d8fee64aaf52bf3d223 0 Negative LFC -1 -
## 8f3c8cb8544640becafb5777eb7a5858 0 Negative LFC -1 -
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 0 Negative LFC -1 -
## 114d0aefa07e7ab7836011f44281b737 0 Negative LFC -1 -
## 582fae36f33acd1efdcaf7cacf00ef0a 1 Positive LFC 1 -
## e655845f5f4ce1633524c0c9a0b15927 1 Positive LFC 1 -
## 93b58b0ba0d326e9c8d1a81f8672c16a 0 Negative LFC -1 -
## 707940842caa2afe60491008e04a8173 1 Positive LFC 1 -
## d16a4faef202f2f76497bdd5a6f454b7 0 Negative LFC -1 -
## 9df251784dde31e05f02b2ee1029d71c 1 Positive LFC 1 -
## prev BH herramienta
## b0553a7cf3e72573824b0dacf2747ae5 no no ALDEX2
## 1c441acb05af7d8fee64aaf52bf3d223 no no ALDEX2
## 8f3c8cb8544640becafb5777eb7a5858 no no ALDEX2
## 1d2b00ff8b7477d2b6c2b8043f7c9d31 no no ALDEX2
## 114d0aefa07e7ab7836011f44281b737 no no ALDEX2
## 582fae36f33acd1efdcaf7cacf00ef0a no no ALDEX2
## e655845f5f4ce1633524c0c9a0b15927 no no ALDEX2
## 93b58b0ba0d326e9c8d1a81f8672c16a no no ALDEX2
## 707940842caa2afe60491008e04a8173 no no ALDEX2
## d16a4faef202f2f76497bdd5a6f454b7 no no ALDEX2
## 9df251784dde31e05f02b2ee1029d71c no no ALDEX2
##
## $res_aldex2_prev_ASV_noBH_df
## feature pval qval wi.ep wi.eBH
## 1 f664a73e7de6e00ff70e013369499cbd 0.020541545 0.8800437 0.050476132 0.9278201
## 2 af2150fef76b295092cd60a788cbe500 0.040464692 1.0000000 0.003428486 0.8575446
## 3 25af29e1b2d121f8aae468d270d75518 0.026012082 0.9991868 0.033069929 0.9735177
## 4 9df251784dde31e05f02b2ee1029d71c 0.004734641 0.9865233 0.000780522 0.5739140
## 5 be1fe9bfe424ad27f568d8e2d7b42c19 0.047431868 1.0000000 0.020298666 0.9778105
## rab.all rab.win.0 rab.win.1 logFC diff.win effect effect.low
## 1 0.8796375 -0.6828906 2.690379 4.371759 6.193586 0.6621493 -2.181100
## 2 7.3793704 5.3620220 7.889656 2.710801 4.502323 0.5448366 -2.675135
## 3 5.0039640 0.2428290 7.853464 6.319063 8.218281 0.7023558 -2.766156
## 4 7.5205554 2.0614681 7.855457 5.763563 5.030301 0.8873925 -0.700537
## 5 6.3887587 1.4669009 6.884206 3.444645 6.861979 0.4985438 -2.905656
## effect.high overlap bacteria enrich_group
## 1 6.786999 0.2627841 g__Lachnospira 1
## 2 9.547727 0.1858157 g__Blautia 1
## 3 7.167203 0.2684660 g__Roseburia 1
## 4 10.321697 0.1631207 g__Fusicatenibacter 1
## 5 7.158569 0.2709220 g__Lachnospiraceae_NK4A136_group 1
## direction orientation qval.txt prev BH herramienta
## 1 Positive LFC 1 - no no ALDEX2
## 2 Positive LFC 1 - no no ALDEX2
## 3 Positive LFC 1 - no no ALDEX2
## 4 Positive LFC 1 - no no ALDEX2
## 5 Positive LFC 1 - no no ALDEX2
##
## $res_aldex2_prev_noBH_df
## feature pval qval wi.ep wi.eBH
## 1 b0553a7cf3e72573824b0dacf2747ae5 0.008519525 0.5478539 0.0335363983 0.8630563
## 2 8f3c8cb8544640becafb5777eb7a5858 0.041246470 0.6191173 0.0159794028 0.5966760
## 3 582fae36f33acd1efdcaf7cacf00ef0a 0.018859728 0.9182940 0.0091581720 0.5668209
## 4 e655845f5f4ce1633524c0c9a0b15927 0.033112543 0.9345265 0.0124168816 0.6119144
## 5 707940842caa2afe60491008e04a8173 0.049927956 0.9994508 0.0167886267 0.7238755
## 6 d16a4faef202f2f76497bdd5a6f454b7 0.036701359 0.5461829 0.0177108014 0.7077479
## 7 9df251784dde31e05f02b2ee1029d71c 0.004806497 0.7188198 0.0009108144 0.1776147
## rab.all rab.win.0 rab.win.1 logFC diff.win effect effect.low
## 1 5.1278786 6.2903223 4.6345668 -1.388413 2.599730 -0.4216156 -10.3205407
## 2 -0.6123744 4.2268388 -1.5903272 -4.827950 6.561867 -0.7033573 -6.8561444
## 3 4.5468408 0.4635261 5.5472130 3.705607 5.679814 0.6579187 -1.9683621
## 4 4.5922986 0.6067977 5.0630297 3.936184 6.131197 0.5976115 -2.7934115
## 5 6.4812773 5.2569354 7.2728346 2.298591 4.017778 0.4983860 -2.1579691
## 6 0.7669586 2.5113131 -0.6537009 -2.770942 4.410486 -0.5739042 -7.9253388
## 7 5.4863544 0.1430596 5.9220551 5.532146 5.206290 0.8894627 -0.7968797
## effect.high overlap bacteria enrich_group
## 1 1.540806 0.2723405 g__Family_XIII_AD3011_group 0
## 2 1.938861 0.2241135 g__Parvimonas 0
## 3 8.670267 0.2255320 g__[Eubacterium]_ventriosum_group 1
## 4 8.837581 0.2372160 g__Lachnospira 1
## 5 8.674798 0.2471591 g__Lachnospiraceae_NK4A136_group 1
## 6 2.570206 0.2453901 o__Oscillospirales_NA 0
## 7 9.299084 0.1590910 g__Fusicatenibacter 1
## direction orientation qval.txt prev BH herramienta
## 1 Negative LFC -1 - no no ALDEX2
## 2 Negative LFC -1 - no no ALDEX2
## 3 Positive LFC 1 - no no ALDEX2
## 4 Positive LFC 1 - no no ALDEX2
## 5 Positive LFC 1 - no no ALDEX2
## 6 Negative LFC -1 - no no ALDEX2
## 7 Positive LFC 1 - no no ALDEX2
##
## $res_ancom_noprev_ASV_BH_df
## bacteria lfc pval qval direction orientation
## 1 g__Lachnospira_NA 1.872501 4.23892e-06 0.02578111 Positive LFC 1
## target_level logFC pvalue padj enrich_group prev BH
## 1 ASV 1.872501 4.23892e-06 0.02578111 1 no yes
## herramienta
## 1 ANCOMBC
##
## $res_ancom_noprev_ASV_noBH_df
## bacteria lfc pval qval
## 1 g__Fusicatenibacter_NA 2.845322 3.486388e-05 3.486388e-05
## 2 g__Roseburia_NA 2.394856 3.350481e-03 3.350481e-03
## 3 g__Faecalibacterium_NA 1.961100 4.794771e-02 4.794771e-02
## 4 g__Lachnospira_NA 1.872501 4.238920e-06 4.238920e-06
## 5 g__Blautia_NA 1.770128 6.613832e-03 6.613832e-03
## 6 g__[Eubacterium]_ventriosum_group_NA 1.753572 1.062581e-02 1.062581e-02
## 7 s__Bacteroides_stercoris 1.398134 9.368408e-03 9.368408e-03
## 8 f__Lachnospiraceae_NA 1.294803 3.632977e-04 3.632977e-04
## 9 f__Lachnospiraceae_NA 1.093063 6.979313e-04 6.979313e-04
## 10 g__Akkermansia_NA 1.079400 7.098286e-03 7.098286e-03
## 11 o__Oscillospirales_NA -1.041650 1.291157e-02 1.291157e-02
## 12 g__Solobacterium_NA -1.308280 4.301887e-02 4.301887e-02
## 13 s__Parabacteroides_merdae -1.495987 4.830643e-02 4.830643e-02
## 14 s__Alistipes_shahii -1.592668 1.419007e-02 1.419007e-02
## 15 g__[Ruminococcus]_torques_group_NA -1.722294 4.158128e-02 4.158128e-02
## 16 g__Parvimonas_NA -2.001690 9.102879e-03 9.102879e-03
## 17 s__Bifidobacterium_longum -2.006056 3.984108e-02 3.984108e-02
## 18 g__[Ruminococcus]_gnavus_group_NA -2.031157 3.322609e-02 3.322609e-02
## 19 g__Negativibacillus_NA -2.038696 7.068300e-03 7.068300e-03
## direction orientation target_level logFC pvalue padj
## 1 Positive LFC 1 ASV 2.845322 3.486388e-05 3.486388e-05
## 2 Positive LFC 1 ASV 2.394856 3.350481e-03 3.350481e-03
## 3 Positive LFC 1 ASV 1.961100 4.794771e-02 4.794771e-02
## 4 Positive LFC 1 ASV 1.872501 4.238920e-06 4.238920e-06
## 5 Positive LFC 1 ASV 1.770128 6.613832e-03 6.613832e-03
## 6 Positive LFC 1 ASV 1.753572 1.062581e-02 1.062581e-02
## 7 Positive LFC 1 ASV 1.398134 9.368408e-03 9.368408e-03
## 8 Positive LFC 1 ASV 1.294803 3.632977e-04 3.632977e-04
## 9 Positive LFC 1 ASV 1.093063 6.979313e-04 6.979313e-04
## 10 Positive LFC 1 ASV 1.079400 7.098286e-03 7.098286e-03
## 11 Negative LFC -1 ASV -1.041650 1.291157e-02 1.291157e-02
## 12 Negative LFC -1 ASV -1.308280 4.301887e-02 4.301887e-02
## 13 Negative LFC -1 ASV -1.495987 4.830643e-02 4.830643e-02
## 14 Negative LFC -1 ASV -1.592668 1.419007e-02 1.419007e-02
## 15 Negative LFC -1 ASV -1.722294 4.158128e-02 4.158128e-02
## 16 Negative LFC -1 ASV -2.001690 9.102879e-03 9.102879e-03
## 17 Negative LFC -1 ASV -2.006056 3.984108e-02 3.984108e-02
## 18 Negative LFC -1 ASV -2.031157 3.322609e-02 3.322609e-02
## 19 Negative LFC -1 ASV -2.038696 7.068300e-03 7.068300e-03
## enrich_group prev BH herramienta
## 1 1 no no ANCOMBC
## 2 1 no no ANCOMBC
## 3 1 no no ANCOMBC
## 4 1 no no ANCOMBC
## 5 1 no no ANCOMBC
## 6 1 no no ANCOMBC
## 7 1 no no ANCOMBC
## 8 1 no no ANCOMBC
## 9 1 no no ANCOMBC
## 10 1 no no ANCOMBC
## 11 0 no no ANCOMBC
## 12 0 no no ANCOMBC
## 13 0 no no ANCOMBC
## 14 0 no no ANCOMBC
## 15 0 no no ANCOMBC
## 16 0 no no ANCOMBC
## 17 0 no no ANCOMBC
## 18 0 no no ANCOMBC
## 19 0 no no ANCOMBC
## noprev: 80 taxa; 22 methods; NA check passed.
## prev: 84 taxa; 26 methods; NA check passed.
The figures presented in this paper focus exclusively on the prevalence-based approach, as this method provides the most accurate and interpretable representation of the results.
Figure 1 (main manuscript) displays a cladogram illustrating the top four unique bacterial taxa identified within each toxicity class. The visualization highlights differences in taxonomic composition across classes based on prevalence patterns. Only taxa identified through the prevalence-based analysis are included.
This figure
was revised and modified in the final version of the manuscript to
improve clarity.
Supplementary Figure 1 presents a Venn diagram illustrating the top four unique bacterial taxa associated with each toxicity class. The comparison is based solely on the prevalence-based analysis.
Supplementary Figure 2 shows a heatmap generated using only the prevalence-derived outputs. The figure highlights distinct abundance and distribution patterns of bacterial taxa across the toxicity classes.
Supplementary Figure 3 provides an alluvial plot illustrating the distribution of oral bacterial genera across toxicity categories. This visualization emphasizes shifts in genus-level composition relative to toxicity status.
## No valid elements detected: res_deseq2_noprev_ASV_BH_df, res_deseq2_noprev_ASV_noBH_df, res_deseq2_noprev_BH_df, res_deseq2_noprev_noBH_df, res_deseq2_prev_ASV_BH_df, res_deseq2_prev_BH_df, res_lefse_cpm_ASV_noprev_BH_df, res_lefse_cpm_prev_ASV_BH_df, res_lefse_cpm_prev_BH_df, res_lefse_ra_ASV_prev_BH_df, res_lefse_ra_noprev_BH_df, res_lefse_ra_prev_BH_df, res_zicoseq_noprev_ASV_BH, res_zicoseq_prev_ASV_BH
Supplementary Figure 4 displays a presence/absence heatmap comparing oral bacterial taxa across the NoPrevalence and Prevalence groups. The figure highlights differences in bacterial occurrence patterns associated with the prevalence classification.
In here we increased the threshold until 0.25, but no feature was retained. Same happened with the Genus approach.
## < table of extent 0 x 0 >
##
## non-significant significant
## 82 1
##
## non-significant significant
## 82 1
##
## non-significant significant
## 82 1
##
## non-significant significant
## 82 1
## Pseudo-count approach is used.
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## taxon_id logFC se qval direction
## 1 8f3c8cb8544640becafb5777eb7a5858 -4.743002 1.0324987 0.009847236 Low
## 2 9df251784dde31e05f02b2ee1029d71c 4.166458 0.8984472 0.009847236 Sev
## qval_txt orientation target_level alpha_grp Genus
## 1 ** -1 tox (sev vs low) 1 g__Parvimonas
## 2 ** 1 tox (sev vs low) 1 g__Fusicatenibacter
## label
## 1 g__Parvimonas
## 2 g__Fusicatenibacter
## Pseudo-count approach is used.
## $toxsev
## [1] "8f3c8cb8544640becafb5777eb7a5858" "358362b29cb4e52ca01e236430b08043"
## [3] "80ab652869a99e7aa6aa94f6963a494a" "9df251784dde31e05f02b2ee1029d71c"
##
## $Sexmale
## character(0)
##
## $Siterectum
## [1] "1d122958ee96131b014bbe0fecba81b1" "ed8b4ed1581ca52b0788f8039536313d"
## [3] "3dc3273da2f47f5733591d9b58cbaf1f" "3b299fbe01dee5fffafb39ed55fd3e78"
## [5] "e59af00273a37b1b1c4708b5971deda2" "82e5115a8f1ead595da52c79fe587645"
## [7] "105344b0b04df4d06036c8c91f439255" "6ee9e6771e438dcdfbbbb4094c40edb8"
## [9] "02bf15068e3e7108f4912f7b2e144b1c" "57339de5c8b87df1e1c2245bb19d8b51"
## [11] "ac07428611cda65054aec775f50cb486" "0991016c9dae25e6dabbd6652d71239a"
##
## $SiteRIGHT
## character(0)
##
## $`Sitetransverse-colon`
## [1] "af3909edeceab197f554e240fe97af83" "f1bf04ec7f805bc268eaddc0c1a1b79a"
## [3] "cb17ef306146fe98440e15e8181ab0a4" "1c441acb05af7d8fee64aaf52bf3d223"
## [5] "27f4cbb6d84d5883800387166ffb6906" "f64256b1969b2f5459213b19bef94eaa"
## [7] "17e293d9412552f2483c73910ff7dd8d" "4400354e1979621239b87e92c5a38a29"
## [9] "8cf427b8122d1849f3b951790cc3dcd3" "747a284c95e1af0ec02d6582f6565774"
## [11] "6630583dd78eb092ece5e17515eb301d" "50ef4b6ac91b2cddcb87cfe5a3b622f8"
## [13] "2f7a13c13416fff5be074cf32995ec44" "cdc5f1f0fcd4c3dd584022d5e28a86c6"
## [15] "186c9fa38e1428e565a3e55fc8483a88" "2bfe6dc4610795667fcc404fad0e6189"
## [17] "3963890db4dddea7f233911bc8a6a079" "ec55f07c37e1dd699e2ca82a3dd57489"
## [19] "1cf8fc19ce34066f96ad6db0dd004b5a" "9af0eb9868f7dac5bfdcd08fd74d293a"
## [21] "020599fbbb43f8c01624f2976599bdcc" "98b6e2d543691237446ced7c52325a2c"
## [23] "37132fca7c4b6ce7b4c34abfa0261664" "c96ab9fa28e0a9a26ee51ac80bbfafde"
## [25] "1ef02b3a08a0fbd22e0d8fe08ab9b0d2" "6fbf9c0e088b6bab28b6b042b495a56d"
## [27] "7bbd42619a8874e3ad59ada1714da878" "b08743e2e152c9e6c117cf375bb04ac1"
## [29] "3f7fd596312c975960542b051cd5cf43" "5581c82e6046e3be5e70a4380bf56b44"
## [31] "8d4dca0e1ab2475b5c5d37bcb46aee5c" "310023723aab4696cdb707dc6bb4d94a"
## [33] "015ce998953e085dd4d5011f7edd05e2" "257a3feb1cfa43f2c55429863b0f16b3"
## [35] "93bd5b75bbf8507566602238d02b8fcf" "7a4b03d408be56f063ae11449f3044a0"
##
## $age_groupover70
## character(0)
##
## $age_groupunder60
## character(0)
## [1] "8f3c8cb8544640becafb5777eb7a5858" "358362b29cb4e52ca01e236430b08043"
## [3] "80ab652869a99e7aa6aa94f6963a494a" "9df251784dde31e05f02b2ee1029d71c"
## [5] "1d122958ee96131b014bbe0fecba81b1" "ed8b4ed1581ca52b0788f8039536313d"
## [7] "3dc3273da2f47f5733591d9b58cbaf1f" "3b299fbe01dee5fffafb39ed55fd3e78"
## [9] "e59af00273a37b1b1c4708b5971deda2" "82e5115a8f1ead595da52c79fe587645"
## [11] "105344b0b04df4d06036c8c91f439255" "6ee9e6771e438dcdfbbbb4094c40edb8"
## [13] "02bf15068e3e7108f4912f7b2e144b1c" "57339de5c8b87df1e1c2245bb19d8b51"
## [15] "ac07428611cda65054aec775f50cb486" "0991016c9dae25e6dabbd6652d71239a"
## [17] "af3909edeceab197f554e240fe97af83" "f1bf04ec7f805bc268eaddc0c1a1b79a"
## [19] "cb17ef306146fe98440e15e8181ab0a4" "1c441acb05af7d8fee64aaf52bf3d223"
## [21] "27f4cbb6d84d5883800387166ffb6906" "f64256b1969b2f5459213b19bef94eaa"
## [23] "17e293d9412552f2483c73910ff7dd8d" "4400354e1979621239b87e92c5a38a29"
## [25] "8cf427b8122d1849f3b951790cc3dcd3" "747a284c95e1af0ec02d6582f6565774"
## [27] "6630583dd78eb092ece5e17515eb301d" "50ef4b6ac91b2cddcb87cfe5a3b622f8"
## [29] "2f7a13c13416fff5be074cf32995ec44" "cdc5f1f0fcd4c3dd584022d5e28a86c6"
## [31] "186c9fa38e1428e565a3e55fc8483a88" "2bfe6dc4610795667fcc404fad0e6189"
## [33] "3963890db4dddea7f233911bc8a6a079" "ec55f07c37e1dd699e2ca82a3dd57489"
## [35] "1cf8fc19ce34066f96ad6db0dd004b5a" "9af0eb9868f7dac5bfdcd08fd74d293a"
## [37] "020599fbbb43f8c01624f2976599bdcc" "98b6e2d543691237446ced7c52325a2c"
## [39] "37132fca7c4b6ce7b4c34abfa0261664" "c96ab9fa28e0a9a26ee51ac80bbfafde"
## [41] "1ef02b3a08a0fbd22e0d8fe08ab9b0d2" "6fbf9c0e088b6bab28b6b042b495a56d"
## [43] "7bbd42619a8874e3ad59ada1714da878" "b08743e2e152c9e6c117cf375bb04ac1"
## [45] "3f7fd596312c975960542b051cd5cf43" "5581c82e6046e3be5e70a4380bf56b44"
## [47] "8d4dca0e1ab2475b5c5d37bcb46aee5c" "310023723aab4696cdb707dc6bb4d94a"
## [49] "015ce998953e085dd4d5011f7edd05e2" "257a3feb1cfa43f2c55429863b0f16b3"
## [51] "93bd5b75bbf8507566602238d02b8fcf" "7a4b03d408be56f063ae11449f3044a0"
## Taxonomy Table: [52 taxa by 7 taxonomic ranks]:
## Domain Phylum
## 8f3c8cb8544640becafb5777eb7a5858 "d__Bacteria" "p__Firmicutes"
## 358362b29cb4e52ca01e236430b08043 "d__Bacteria" "p__Firmicutes"
## 80ab652869a99e7aa6aa94f6963a494a "d__Bacteria" "p__Firmicutes"
## 9df251784dde31e05f02b2ee1029d71c "d__Bacteria" "p__Firmicutes"
## 1d122958ee96131b014bbe0fecba81b1 "d__Bacteria" "p__Actinobacteriota"
## ed8b4ed1581ca52b0788f8039536313d "d__Bacteria" "p__Bacteroidota"
## 3dc3273da2f47f5733591d9b58cbaf1f "d__Bacteria" "p__Bacteroidota"
## 3b299fbe01dee5fffafb39ed55fd3e78 "d__Bacteria" "p__Bacteroidota"
## e59af00273a37b1b1c4708b5971deda2 "d__Bacteria" "p__Synergistota"
## 82e5115a8f1ead595da52c79fe587645 "d__Bacteria" "p__Firmicutes"
## 105344b0b04df4d06036c8c91f439255 "d__Bacteria" "p__Firmicutes"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "d__Bacteria" "p__Firmicutes"
## 02bf15068e3e7108f4912f7b2e144b1c "d__Bacteria" "p__Firmicutes"
## 57339de5c8b87df1e1c2245bb19d8b51 "d__Bacteria" "p__Firmicutes"
## ac07428611cda65054aec775f50cb486 "d__Bacteria" "p__Firmicutes"
## 0991016c9dae25e6dabbd6652d71239a "d__Bacteria" "p__Firmicutes"
## af3909edeceab197f554e240fe97af83 "d__Bacteria" "p__Bacteroidota"
## f1bf04ec7f805bc268eaddc0c1a1b79a "d__Bacteria" "p__Bacteroidota"
## cb17ef306146fe98440e15e8181ab0a4 "d__Bacteria" "p__Bacteroidota"
## 1c441acb05af7d8fee64aaf52bf3d223 "d__Bacteria" "p__Firmicutes"
## 27f4cbb6d84d5883800387166ffb6906 "d__Bacteria" "p__Firmicutes"
## f64256b1969b2f5459213b19bef94eaa "d__Bacteria" "p__Firmicutes"
## 17e293d9412552f2483c73910ff7dd8d "d__Bacteria" "p__Firmicutes"
## 4400354e1979621239b87e92c5a38a29 "d__Bacteria" "p__Firmicutes"
## 8cf427b8122d1849f3b951790cc3dcd3 "d__Bacteria" "p__Firmicutes"
## 747a284c95e1af0ec02d6582f6565774 "d__Bacteria" "p__Firmicutes"
## 6630583dd78eb092ece5e17515eb301d "d__Bacteria" "p__Firmicutes"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "d__Bacteria" "p__Firmicutes"
## 2f7a13c13416fff5be074cf32995ec44 "d__Bacteria" "p__Firmicutes"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "d__Bacteria" "p__Firmicutes"
## 186c9fa38e1428e565a3e55fc8483a88 "d__Bacteria" "p__Firmicutes"
## 2bfe6dc4610795667fcc404fad0e6189 "d__Bacteria" "p__Firmicutes"
## 3963890db4dddea7f233911bc8a6a079 "d__Bacteria" "p__Firmicutes"
## ec55f07c37e1dd699e2ca82a3dd57489 "d__Bacteria" "p__Firmicutes"
## 1cf8fc19ce34066f96ad6db0dd004b5a "d__Bacteria" "p__Firmicutes"
## 9af0eb9868f7dac5bfdcd08fd74d293a "d__Bacteria" "p__Firmicutes"
## 020599fbbb43f8c01624f2976599bdcc "d__Bacteria" "p__Firmicutes"
## 98b6e2d543691237446ced7c52325a2c "d__Bacteria" "p__Firmicutes"
## 37132fca7c4b6ce7b4c34abfa0261664 "d__Bacteria" "p__Firmicutes"
## c96ab9fa28e0a9a26ee51ac80bbfafde "d__Bacteria" "p__Firmicutes"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "d__Bacteria" "p__Firmicutes"
## 6fbf9c0e088b6bab28b6b042b495a56d "d__Bacteria" "p__Firmicutes"
## 7bbd42619a8874e3ad59ada1714da878 "d__Bacteria" "p__Firmicutes"
## b08743e2e152c9e6c117cf375bb04ac1 "d__Bacteria" "p__Firmicutes"
## 3f7fd596312c975960542b051cd5cf43 "d__Bacteria" "p__Firmicutes"
## 5581c82e6046e3be5e70a4380bf56b44 "d__Bacteria" "p__Firmicutes"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "d__Bacteria" "p__Firmicutes"
## 310023723aab4696cdb707dc6bb4d94a "d__Bacteria" "p__Firmicutes"
## 015ce998953e085dd4d5011f7edd05e2 "d__Bacteria" "p__Firmicutes"
## 257a3feb1cfa43f2c55429863b0f16b3 "d__Bacteria" "p__Firmicutes"
## 93bd5b75bbf8507566602238d02b8fcf "d__Bacteria" "p__Firmicutes"
## 7a4b03d408be56f063ae11449f3044a0 "d__Bacteria" "p__Firmicutes"
## Class
## 8f3c8cb8544640becafb5777eb7a5858 "c__Clostridia"
## 358362b29cb4e52ca01e236430b08043 "c__Incertae_Sedis"
## 80ab652869a99e7aa6aa94f6963a494a "c__Clostridia"
## 9df251784dde31e05f02b2ee1029d71c "c__Clostridia"
## 1d122958ee96131b014bbe0fecba81b1 "c__Actinobacteria"
## ed8b4ed1581ca52b0788f8039536313d "c__Bacteroidia"
## 3dc3273da2f47f5733591d9b58cbaf1f "c__Bacteroidia"
## 3b299fbe01dee5fffafb39ed55fd3e78 "c__Bacteroidia"
## e59af00273a37b1b1c4708b5971deda2 "c__Synergistia"
## 82e5115a8f1ead595da52c79fe587645 "c__Bacilli"
## 105344b0b04df4d06036c8c91f439255 "c__Clostridia"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "c__Clostridia"
## 02bf15068e3e7108f4912f7b2e144b1c "c__Clostridia"
## 57339de5c8b87df1e1c2245bb19d8b51 "c__Clostridia"
## ac07428611cda65054aec775f50cb486 "c__Clostridia"
## 0991016c9dae25e6dabbd6652d71239a "c__Clostridia"
## af3909edeceab197f554e240fe97af83 "c__Bacteroidia"
## f1bf04ec7f805bc268eaddc0c1a1b79a "c__Bacteroidia"
## cb17ef306146fe98440e15e8181ab0a4 "c__Bacteroidia"
## 1c441acb05af7d8fee64aaf52bf3d223 "c__Clostridia"
## 27f4cbb6d84d5883800387166ffb6906 "c__Clostridia"
## f64256b1969b2f5459213b19bef94eaa "c__Bacilli"
## 17e293d9412552f2483c73910ff7dd8d "c__Clostridia"
## 4400354e1979621239b87e92c5a38a29 "c__Clostridia"
## 8cf427b8122d1849f3b951790cc3dcd3 "c__Clostridia"
## 747a284c95e1af0ec02d6582f6565774 "c__Clostridia"
## 6630583dd78eb092ece5e17515eb301d "c__Clostridia"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "c__Clostridia"
## 2f7a13c13416fff5be074cf32995ec44 "c__Clostridia"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "c__Clostridia"
## 186c9fa38e1428e565a3e55fc8483a88 "c__Clostridia"
## 2bfe6dc4610795667fcc404fad0e6189 "c__Clostridia"
## 3963890db4dddea7f233911bc8a6a079 "c__Clostridia"
## ec55f07c37e1dd699e2ca82a3dd57489 "c__Clostridia"
## 1cf8fc19ce34066f96ad6db0dd004b5a "c__Clostridia"
## 9af0eb9868f7dac5bfdcd08fd74d293a "c__Clostridia"
## 020599fbbb43f8c01624f2976599bdcc "c__Clostridia"
## 98b6e2d543691237446ced7c52325a2c "c__Clostridia"
## 37132fca7c4b6ce7b4c34abfa0261664 "c__Clostridia"
## c96ab9fa28e0a9a26ee51ac80bbfafde "c__Clostridia"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "c__Clostridia"
## 6fbf9c0e088b6bab28b6b042b495a56d "c__Clostridia"
## 7bbd42619a8874e3ad59ada1714da878 "c__Clostridia"
## b08743e2e152c9e6c117cf375bb04ac1 "c__Clostridia"
## 3f7fd596312c975960542b051cd5cf43 "c__Clostridia"
## 5581c82e6046e3be5e70a4380bf56b44 "c__Clostridia"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "c__Clostridia"
## 310023723aab4696cdb707dc6bb4d94a "c__Clostridia"
## 015ce998953e085dd4d5011f7edd05e2 "c__Clostridia"
## 257a3feb1cfa43f2c55429863b0f16b3 "c__Clostridia"
## 93bd5b75bbf8507566602238d02b8fcf "c__Clostridia"
## 7a4b03d408be56f063ae11449f3044a0 "c__Clostridia"
## Order
## 8f3c8cb8544640becafb5777eb7a5858 "o__Peptostreptococcales-Tissierellales"
## 358362b29cb4e52ca01e236430b08043 "o__DTU014"
## 80ab652869a99e7aa6aa94f6963a494a "o__Christensenellales"
## 9df251784dde31e05f02b2ee1029d71c "o__Lachnospirales"
## 1d122958ee96131b014bbe0fecba81b1 "o__Actinomycetales"
## ed8b4ed1581ca52b0788f8039536313d "o__Bacteroidales"
## 3dc3273da2f47f5733591d9b58cbaf1f "o__Bacteroidales"
## 3b299fbe01dee5fffafb39ed55fd3e78 "o__Bacteroidales"
## e59af00273a37b1b1c4708b5971deda2 "o__Synergistales"
## 82e5115a8f1ead595da52c79fe587645 "o__Erysipelotrichales"
## 105344b0b04df4d06036c8c91f439255 "o__Lachnospirales"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "o__Lachnospirales"
## 02bf15068e3e7108f4912f7b2e144b1c "o__Oscillospirales"
## 57339de5c8b87df1e1c2245bb19d8b51 "o__Christensenellales"
## ac07428611cda65054aec775f50cb486 "o__Christensenellales"
## 0991016c9dae25e6dabbd6652d71239a "o__Lachnospirales"
## af3909edeceab197f554e240fe97af83 "o__Bacteroidales"
## f1bf04ec7f805bc268eaddc0c1a1b79a "o__Bacteroidales"
## cb17ef306146fe98440e15e8181ab0a4 "o__Bacteroidales"
## 1c441acb05af7d8fee64aaf52bf3d223 "o__Peptostreptococcales-Tissierellales"
## 27f4cbb6d84d5883800387166ffb6906 "o__Peptostreptococcales-Tissierellales"
## f64256b1969b2f5459213b19bef94eaa "o__RF39"
## 17e293d9412552f2483c73910ff7dd8d "o__Oscillospirales"
## 4400354e1979621239b87e92c5a38a29 "o__Lachnospirales"
## 8cf427b8122d1849f3b951790cc3dcd3 "o__Clostridia_UCG-014"
## 747a284c95e1af0ec02d6582f6565774 "o__Clostridia_UCG-014"
## 6630583dd78eb092ece5e17515eb301d "o__Lachnospirales"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "o__Lachnospirales"
## 2f7a13c13416fff5be074cf32995ec44 "o__Lachnospirales"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "o__Oscillospirales"
## 186c9fa38e1428e565a3e55fc8483a88 "o__Oscillospirales"
## 2bfe6dc4610795667fcc404fad0e6189 "o__Oscillospirales"
## 3963890db4dddea7f233911bc8a6a079 "o__Oscillospirales"
## ec55f07c37e1dd699e2ca82a3dd57489 "o__Oscillospirales"
## 1cf8fc19ce34066f96ad6db0dd004b5a "o__Oscillospirales"
## 9af0eb9868f7dac5bfdcd08fd74d293a "o__Oscillospirales"
## 020599fbbb43f8c01624f2976599bdcc "o__Oscillospirales"
## 98b6e2d543691237446ced7c52325a2c "o__Oscillospirales"
## 37132fca7c4b6ce7b4c34abfa0261664 "o__Oscillospirales"
## c96ab9fa28e0a9a26ee51ac80bbfafde "o__Christensenellales"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "o__Christensenellales"
## 6fbf9c0e088b6bab28b6b042b495a56d "o__Peptococcales"
## 7bbd42619a8874e3ad59ada1714da878 "o__Christensenellales"
## b08743e2e152c9e6c117cf375bb04ac1 "o__Oscillospirales"
## 3f7fd596312c975960542b051cd5cf43 "o__Oscillospirales"
## 5581c82e6046e3be5e70a4380bf56b44 "o__Oscillospirales"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "o__Oscillospirales"
## 310023723aab4696cdb707dc6bb4d94a "o__Oscillospirales"
## 015ce998953e085dd4d5011f7edd05e2 "o__Oscillospirales"
## 257a3feb1cfa43f2c55429863b0f16b3 "o__Oscillospirales"
## 93bd5b75bbf8507566602238d02b8fcf "o__Oscillospirales"
## 7a4b03d408be56f063ae11449f3044a0 "o__Lachnospirales"
## Family
## 8f3c8cb8544640becafb5777eb7a5858 "f__Family_XI"
## 358362b29cb4e52ca01e236430b08043 "f__DTU014"
## 80ab652869a99e7aa6aa94f6963a494a "f__Christensenellaceae"
## 9df251784dde31e05f02b2ee1029d71c "f__Lachnospiraceae"
## 1d122958ee96131b014bbe0fecba81b1 "f__Actinomycetaceae"
## ed8b4ed1581ca52b0788f8039536313d "f__Rikenellaceae"
## 3dc3273da2f47f5733591d9b58cbaf1f "f__Tannerellaceae"
## 3b299fbe01dee5fffafb39ed55fd3e78 "f__Barnesiellaceae"
## e59af00273a37b1b1c4708b5971deda2 "f__Synergistaceae"
## 82e5115a8f1ead595da52c79fe587645 "f__Erysipelotrichaceae"
## 105344b0b04df4d06036c8c91f439255 "f__Lachnospiraceae"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "f__Lachnospiraceae"
## 02bf15068e3e7108f4912f7b2e144b1c "f__Oscillospiraceae"
## 57339de5c8b87df1e1c2245bb19d8b51 "f__Christensenellaceae"
## ac07428611cda65054aec775f50cb486 "f__Christensenellaceae"
## 0991016c9dae25e6dabbd6652d71239a "f__Lachnospiraceae"
## af3909edeceab197f554e240fe97af83 "f__Rikenellaceae"
## f1bf04ec7f805bc268eaddc0c1a1b79a "f__Bacteroidaceae"
## cb17ef306146fe98440e15e8181ab0a4 "f__Tannerellaceae"
## 1c441acb05af7d8fee64aaf52bf3d223 "f__Anaerovoracaceae"
## 27f4cbb6d84d5883800387166ffb6906 "f__Anaerovoracaceae"
## f64256b1969b2f5459213b19bef94eaa "f__RF39"
## 17e293d9412552f2483c73910ff7dd8d "f__Butyricicoccaceae"
## 4400354e1979621239b87e92c5a38a29 "f__Lachnospiraceae"
## 8cf427b8122d1849f3b951790cc3dcd3 "f__Clostridia_UCG-014"
## 747a284c95e1af0ec02d6582f6565774 "f__Clostridia_UCG-014"
## 6630583dd78eb092ece5e17515eb301d "f__Lachnospiraceae"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "f__Lachnospiraceae"
## 2f7a13c13416fff5be074cf32995ec44 "f__Lachnospiraceae"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "f__Oscillospiraceae"
## 186c9fa38e1428e565a3e55fc8483a88 "f__Oscillospiraceae"
## 2bfe6dc4610795667fcc404fad0e6189 "f__Ruminococcaceae"
## 3963890db4dddea7f233911bc8a6a079 "f__Ruminococcaceae"
## ec55f07c37e1dd699e2ca82a3dd57489 "f__Ruminococcaceae"
## 1cf8fc19ce34066f96ad6db0dd004b5a "f__Ruminococcaceae"
## 9af0eb9868f7dac5bfdcd08fd74d293a "f__Ruminococcaceae"
## 020599fbbb43f8c01624f2976599bdcc "f__Ruminococcaceae"
## 98b6e2d543691237446ced7c52325a2c "f__Ruminococcaceae"
## 37132fca7c4b6ce7b4c34abfa0261664 "f__Oscillospiraceae"
## c96ab9fa28e0a9a26ee51ac80bbfafde "f__Christensenellaceae"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "f__Christensenellaceae"
## 6fbf9c0e088b6bab28b6b042b495a56d "f__Peptococcaceae"
## 7bbd42619a8874e3ad59ada1714da878 "f__Christensenellaceae"
## b08743e2e152c9e6c117cf375bb04ac1 "o__Oscillospirales_NA"
## 3f7fd596312c975960542b051cd5cf43 "f__Ruminococcaceae"
## 5581c82e6046e3be5e70a4380bf56b44 "f__Ruminococcaceae"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "f__Ruminococcaceae"
## 310023723aab4696cdb707dc6bb4d94a "f__Ruminococcaceae"
## 015ce998953e085dd4d5011f7edd05e2 "f__Ruminococcaceae"
## 257a3feb1cfa43f2c55429863b0f16b3 "f__[Eubacterium]_coprostanoligenes_group"
## 93bd5b75bbf8507566602238d02b8fcf "f__[Eubacterium]_coprostanoligenes_group"
## 7a4b03d408be56f063ae11449f3044a0 "f__Lachnospiraceae"
## Genus
## 8f3c8cb8544640becafb5777eb7a5858 "g__Parvimonas"
## 358362b29cb4e52ca01e236430b08043 "g__DTU014"
## 80ab652869a99e7aa6aa94f6963a494a "g__Christensenellaceae_R-7_group"
## 9df251784dde31e05f02b2ee1029d71c "g__Fusicatenibacter"
## 1d122958ee96131b014bbe0fecba81b1 "g__Actinomyces"
## ed8b4ed1581ca52b0788f8039536313d "g__Alistipes"
## 3dc3273da2f47f5733591d9b58cbaf1f "g__Parabacteroides"
## 3b299fbe01dee5fffafb39ed55fd3e78 "g__uncultured"
## e59af00273a37b1b1c4708b5971deda2 "g__Cloacibacillus"
## 82e5115a8f1ead595da52c79fe587645 "g__Solobacterium"
## 105344b0b04df4d06036c8c91f439255 "g__Blautia"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "g__Lachnospiraceae_UCG-003"
## 02bf15068e3e7108f4912f7b2e144b1c "g__UCG-002"
## 57339de5c8b87df1e1c2245bb19d8b51 "g__Christensenellaceae_R-7_group"
## ac07428611cda65054aec775f50cb486 "g__Christensenellaceae_R-7_group"
## 0991016c9dae25e6dabbd6652d71239a "g__Fusicatenibacter"
## af3909edeceab197f554e240fe97af83 "g__Alistipes"
## f1bf04ec7f805bc268eaddc0c1a1b79a "g__Bacteroides"
## cb17ef306146fe98440e15e8181ab0a4 "g__Parabacteroides"
## 1c441acb05af7d8fee64aaf52bf3d223 "g__[Eubacterium]_nodatum_group"
## 27f4cbb6d84d5883800387166ffb6906 "f__Anaerovoracaceae_NA"
## f64256b1969b2f5459213b19bef94eaa "g__RF39"
## 17e293d9412552f2483c73910ff7dd8d "g__Butyricicoccus"
## 4400354e1979621239b87e92c5a38a29 "g__Roseburia"
## 8cf427b8122d1849f3b951790cc3dcd3 "g__Clostridia_UCG-014"
## 747a284c95e1af0ec02d6582f6565774 "g__Clostridia_UCG-014"
## 6630583dd78eb092ece5e17515eb301d "f__Lachnospiraceae_NA"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "g__Sellimonas"
## 2f7a13c13416fff5be074cf32995ec44 "f__Lachnospiraceae_NA"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "g__UCG-002"
## 186c9fa38e1428e565a3e55fc8483a88 "g__uncultured"
## 2bfe6dc4610795667fcc404fad0e6189 "g__Incertae_Sedis"
## 3963890db4dddea7f233911bc8a6a079 "g__Caproiciproducens"
## ec55f07c37e1dd699e2ca82a3dd57489 "g__uncultured"
## 1cf8fc19ce34066f96ad6db0dd004b5a "g__DTU089"
## 9af0eb9868f7dac5bfdcd08fd74d293a "g__DTU089"
## 020599fbbb43f8c01624f2976599bdcc "g__CAG-352"
## 98b6e2d543691237446ced7c52325a2c "g__Subdoligranulum"
## 37132fca7c4b6ce7b4c34abfa0261664 "g__Papillibacter"
## c96ab9fa28e0a9a26ee51ac80bbfafde "g__Christensenellaceae_R-7_group"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "g__Christensenellaceae_R-7_group"
## 6fbf9c0e088b6bab28b6b042b495a56d "g__uncultured"
## 7bbd42619a8874e3ad59ada1714da878 "g__uncultured"
## b08743e2e152c9e6c117cf375bb04ac1 "o__Oscillospirales_NA"
## 3f7fd596312c975960542b051cd5cf43 "g__Candidatus_Soleaferrea"
## 5581c82e6046e3be5e70a4380bf56b44 "f__Ruminococcaceae_NA"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "g__Paludicola"
## 310023723aab4696cdb707dc6bb4d94a "g__Fournierella"
## 015ce998953e085dd4d5011f7edd05e2 "g__uncultured"
## 257a3feb1cfa43f2c55429863b0f16b3 "g__[Eubacterium]_coprostanoligenes_group"
## 93bd5b75bbf8507566602238d02b8fcf "g__[Eubacterium]_coprostanoligenes_group"
## 7a4b03d408be56f063ae11449f3044a0 "f__Lachnospiraceae_NA"
## Species
## 8f3c8cb8544640becafb5777eb7a5858 "g__Parvimonas_NA"
## 358362b29cb4e52ca01e236430b08043 "s__unidentified"
## 80ab652869a99e7aa6aa94f6963a494a "g__Christensenellaceae_R-7_group_NA"
## 9df251784dde31e05f02b2ee1029d71c "g__Fusicatenibacter_NA"
## 1d122958ee96131b014bbe0fecba81b1 "s__Schaalia_cardiffensis"
## ed8b4ed1581ca52b0788f8039536313d "s__Alistipes_shahii"
## 3dc3273da2f47f5733591d9b58cbaf1f "s__Parabacteroides_merdae"
## 3b299fbe01dee5fffafb39ed55fd3e78 "g__uncultured_NA"
## e59af00273a37b1b1c4708b5971deda2 "s__Cloacibacillus_porcorum"
## 82e5115a8f1ead595da52c79fe587645 "s__uncultured_bacterium"
## 105344b0b04df4d06036c8c91f439255 "g__Blautia_NA"
## 6ee9e6771e438dcdfbbbb4094c40edb8 "s__uncultured_bacterium"
## 02bf15068e3e7108f4912f7b2e144b1c "s__uncultured_organism"
## 57339de5c8b87df1e1c2245bb19d8b51 "g__Christensenellaceae_R-7_group_NA"
## ac07428611cda65054aec775f50cb486 "g__Christensenellaceae_R-7_group_NA"
## 0991016c9dae25e6dabbd6652d71239a "g__Fusicatenibacter_NA"
## af3909edeceab197f554e240fe97af83 "s__Alistipes_inops"
## f1bf04ec7f805bc268eaddc0c1a1b79a "g__Bacteroides_NA"
## cb17ef306146fe98440e15e8181ab0a4 "s__Parabacteroides_johnsonii"
## 1c441acb05af7d8fee64aaf52bf3d223 "s__uncultured_bacterium"
## 27f4cbb6d84d5883800387166ffb6906 "f__Anaerovoracaceae_NA"
## f64256b1969b2f5459213b19bef94eaa "g__RF39_NA"
## 17e293d9412552f2483c73910ff7dd8d "g__Butyricicoccus_NA"
## 4400354e1979621239b87e92c5a38a29 "g__Roseburia_NA"
## 8cf427b8122d1849f3b951790cc3dcd3 "g__Clostridia_UCG-014_NA"
## 747a284c95e1af0ec02d6582f6565774 "g__Clostridia_UCG-014_NA"
## 6630583dd78eb092ece5e17515eb301d "f__Lachnospiraceae_NA"
## 50ef4b6ac91b2cddcb87cfe5a3b622f8 "s__Lachnoclostridium_phocaeense"
## 2f7a13c13416fff5be074cf32995ec44 "f__Lachnospiraceae_NA"
## cdc5f1f0fcd4c3dd584022d5e28a86c6 "s__uncultured_rumen"
## 186c9fa38e1428e565a3e55fc8483a88 "g__uncultured_NA"
## 2bfe6dc4610795667fcc404fad0e6189 "s__uncultured_bacterium"
## 3963890db4dddea7f233911bc8a6a079 "g__Caproiciproducens_NA"
## ec55f07c37e1dd699e2ca82a3dd57489 "g__uncultured_NA"
## 1cf8fc19ce34066f96ad6db0dd004b5a "g__DTU089_NA"
## 9af0eb9868f7dac5bfdcd08fd74d293a "g__DTU089_NA"
## 020599fbbb43f8c01624f2976599bdcc "s__uncultured_bacterium"
## 98b6e2d543691237446ced7c52325a2c "g__Subdoligranulum_NA"
## 37132fca7c4b6ce7b4c34abfa0261664 "s__uncultured_bacterium"
## c96ab9fa28e0a9a26ee51ac80bbfafde "g__Christensenellaceae_R-7_group_NA"
## 1ef02b3a08a0fbd22e0d8fe08ab9b0d2 "g__Christensenellaceae_R-7_group_NA"
## 6fbf9c0e088b6bab28b6b042b495a56d "g__uncultured_NA"
## 7bbd42619a8874e3ad59ada1714da878 "s__uncultured_bacterium"
## b08743e2e152c9e6c117cf375bb04ac1 "o__Oscillospirales_NA"
## 3f7fd596312c975960542b051cd5cf43 "s__uncultured_bacterium"
## 5581c82e6046e3be5e70a4380bf56b44 "f__Ruminococcaceae_NA"
## 8d4dca0e1ab2475b5c5d37bcb46aee5c "s__uncultured_bacterium"
## 310023723aab4696cdb707dc6bb4d94a "s__uncultured_organism"
## 015ce998953e085dd4d5011f7edd05e2 "s__human_gut"
## 257a3feb1cfa43f2c55429863b0f16b3 "s__gut_metagenome"
## 93bd5b75bbf8507566602238d02b8fcf "s__Eubacteriaceae_bacterium"
## 7a4b03d408be56f063ae11449f3044a0 "f__Lachnospiraceae_NA"
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## taxon_id logFC se qval direction
## 1 8f3c8cb8544640becafb5777eb7a5858 -4.496979 1.0140375 0.0369338 Low
## 2 358362b29cb4e52ca01e236430b08043 -1.349932 0.3065764 0.0369338 Low
## 3 80ab652869a99e7aa6aa94f6963a494a -1.349932 0.3065764 0.0369338 Low
## 4 9df251784dde31e05f02b2ee1029d71c 3.995333 0.8513157 0.0369338 Sev
## qval_txt orientation target_level alpha_grp Genus
## 1 * -1 tox (sev vs low) 0.6 g__Parvimonas
## 2 * -1 tox (sev vs low) 0.6 <NA>
## 3 * -1 tox (sev vs low) 0.6 <NA>
## 4 * 1 tox (sev vs low) 0.6 g__Fusicatenibacter
## label
## 1 g__Parvimonas
## 2 358362b29cb4e52ca01e236430b08043
## 3 80ab652869a99e7aa6aa94f6963a494a
## 4 g__Fusicatenibacter
## Pseudo-count approach is used.
## taxon_id logFC se pval
## 1 8f3c8cb8544640becafb5777eb7a5858 -5.0425457 0.9997091 2.458894e-05
## 2 2f48ab3f1c064ad0cc3d56842b592031 -4.2707063 1.2954248 2.662394e-03
## 3 1d2b00ff8b7477d2b6c2b8043f7c9d31 -3.5378299 1.0093133 1.555353e-03
## 4 11902be998c8092e98cbe98af2f648af -3.4433137 1.4641530 2.595165e-02
## 5 114d0aefa07e7ab7836011f44281b737 -3.1942050 0.9283221 1.838003e-03
## 6 6343c15ef6a0b28bb8d019ebbcd0a55a -3.0537582 1.4286737 4.143148e-02
## 7 d16a4faef202f2f76497bdd5a6f454b7 -2.7360674 0.8189631 2.377946e-03
## 8 dd9711b1499af66948ee3697968d698c -2.6275397 0.7876375 2.408120e-03
## 9 bd636d8da2dca9bb0b1aa1da7e88890d -2.5717471 0.8020960 3.351394e-03
## 10 cc465b18f37cb7e609cff5ba5ed2bffe -2.4444538 1.1968956 5.063823e-02
## 11 fab1c84c9592bdd07aab0fed81cac039 -2.4174912 0.8210726 6.444136e-03
## 12 b0553a7cf3e72573824b0dacf2747ae5 -2.2029776 0.9421536 2.674508e-02
## 13 df796b258d978ffc2ff31467cda2f996 -2.1201946 1.2613875 1.039172e-01
## 14 478739167292e4f93f1eb7aceb2e3e81 -2.0668687 1.4905123 1.764805e-01
## 15 78c2b8e06a58a133919d9e5fd0cd2da1 -2.0622355 0.6211206 2.507560e-03
## 16 8f6cab269b0e9c75fb86b4e1f1046fbd -2.0442092 0.7664573 1.257079e-02
## 17 134d27c5f6a976ed0421b8e0581d4696 -2.0272602 1.1137261 7.942666e-02
## 18 089a1f1f72e5358297250366530029ed -1.9887385 1.0486525 6.825913e-02
## 19 1c441acb05af7d8fee64aaf52bf3d223 -1.9851335 0.8852837 3.303957e-02
## 20 8ca6b78b226fb18a090b1df68fb9edd9 -1.9724367 1.2576288 1.280263e-01
## 21 4df8473b0be4ef1e59f0b0709c586919 -1.9288835 1.1437874 1.028316e-01
## 22 f047aa1950d5df8c5f644fc3e0816c7a -1.8334816 0.8227024 3.404550e-02
## 23 8e5b84dcbc738d004995e37ef8fb41c0 -1.8074723 1.3098679 1.785409e-01
## 24 b52cd8c186d511e8e4bad72d8bfbf0ed -1.7994300 0.8831979 5.116065e-02
## 25 93b58b0ba0d326e9c8d1a81f8672c16a -1.7704855 0.5631732 3.924189e-03
## 26 310023723aab4696cdb707dc6bb4d94a -1.7587118 0.9968910 8.860782e-02
## 27 e15b6ef1cd643dff3f0649b7baba06e8 -1.7355239 1.1163694 1.312696e-01
## 28 31c7bc067538dcf7916e8b7cbefa44e5 -1.7100218 1.3262709 2.078276e-01
## 29 1c4b27dbe152290cd541bc61cf2e32ff -1.6905130 0.7400302 3.013220e-02
## 30 d0377d9209ed8843c1b85df7bc622a2c -1.6854215 1.3480697 2.215573e-01
## 31 02e1e971506b265659c0f5bf758c24b7 -1.6705025 1.0917879 1.372221e-01
## 32 6dae81dec55dfbe3d76b5f031f625cd7 -1.6579210 0.6646975 1.879734e-02
## 33 3534b4be198dac95882f6e730b647c60 -1.5824800 1.0606952 1.469004e-01
## 34 4d6fe682a4dd9aad8decfab830a193e0 -1.5026061 1.0169023 1.506677e-01
## 35 19ff2a801156e1930f697799d400fa95 -1.4971766 1.2792802 2.517343e-01
## 36 119e51e38e589ed2462a6199598f66da -1.4635241 0.5249683 9.427226e-03
## 37 09ad3f07c79e7bbc78206689dc55492d -1.4476238 1.0825907 1.919249e-01
## 38 f2cbb29998f80ea0d81f9cde98ee136e -1.4437358 1.1487358 2.192084e-01
## 39 7b64ebaf4ce0974210877b3efd3f2406 -1.4097703 1.2714581 2.769573e-01
## 40 0b9d6ae30698fe3144a187eaaafdaef8 -1.3914469 1.2030135 2.571949e-01
## 41 08e91014e3d9c1c1329d013f141cd5bd -1.3813777 1.1416981 2.364209e-01
## 42 9e7125a102db8951720ec539b4e78113 -1.3695076 1.3042695 3.026882e-01
## 43 57bab9df7394498748730d8b1613bf45 -1.2821949 0.5101564 1.799090e-02
## 44 da8b26f82eb70e299518e149ae85f3d9 -1.2620121 1.0270087 2.293661e-01
## 45 7931ef4ba7852a196ef9674b8fc64631 -1.2160249 1.1604286 3.036418e-01
## 46 df3d6113f855e22f2a6d44e60f01baa7 -1.1872410 1.1263427 3.008634e-01
## 47 2215fda718f2798e7e94648af4e01b1c -1.1827621 0.4045982 6.784998e-03
## 48 7a1a7d3362a2f4e3bb2b0f088b9a6b84 -1.1589601 0.6011674 6.407265e-02
## 49 3d48874183df65f00f253cf68234e2ca -1.0817522 1.6824310 5.254746e-01
## 50 ffc36e27c82042664a16bcd4d380b286 -1.0813450 1.7407385 5.394934e-01
## 51 dae883c8aea73654e4481627e671e183 -1.0805421 1.2525887 3.956603e-01
## 52 762c20336116b7b3237d0c9ecadb8759 -1.0588756 0.5177663 5.034802e-02
## 53 15e05255b2aa8ee3524ca61eb207bb18 -1.0415531 0.9084611 2.612909e-01
## 54 41ed97af33c5600c6277ed863c6dbba3 -1.0375216 0.4894613 4.302709e-02
## 55 9b90d16e70c628766310ca5d53bd7e86 -1.0140064 0.6932397 1.546828e-01
## 56 e59af00273a37b1b1c4708b5971deda2 -0.9915057 0.4376135 3.139454e-02
## 57 b61ab877a65a7d6819bd4ac9c60edb91 1.0398881 0.6455833 1.184465e-01
## 58 4a1547af0bbae2aff8f3222d2fba2102 1.0778981 1.3425161 4.287990e-01
## 59 f44c7de06be4c6f9cbe32fa9ffbb30b9 1.0843863 0.9340677 2.554730e-01
## 60 df8f768b0e149cc5c7a6f5cf15cd6fa8 1.0927057 1.0854519 3.227082e-01
## 61 18eb929cf9350e0af65cf863c2786858 1.1745112 0.8693992 1.875295e-01
## 62 4608cf2aa614ad44e2e4c90e5145221e 1.1978138 1.5694424 4.517212e-01
## 63 fb7a1c0b3625f00ee42dbfcbaa001f12 1.3697600 1.7007227 4.273765e-01
## 64 e9398bc0ad9626bc4742c93fd0363bac 1.4467848 1.3477420 2.922170e-01
## 65 4516aa60a483dd8c7bbc57098c45f1a5 1.4495658 1.0769593 1.891064e-01
## 66 3f3a0eaeea9c0690b6ede1b17b4fd8ce 1.5659306 1.5023892 3.061919e-01
## 67 25af29e1b2d121f8aae468d270d75518 1.5948703 1.3240017 2.384477e-01
## 68 b264ac8ff5f9aff74f0b9aa084d9a9f0 1.6077512 1.2479526 2.081824e-01
## 69 707940842caa2afe60491008e04a8173 1.7429777 0.9858580 8.796081e-02
## 70 582fae36f33acd1efdcaf7cacf00ef0a 2.5427157 1.1118125 2.995904e-02
## 71 e655845f5f4ce1633524c0c9a0b15927 2.7093556 1.3233373 5.010882e-02
## 72 9df251784dde31e05f02b2ee1029d71c 4.1136620 0.8902026 7.811807e-05
## qval direction qval_txt orientation target_level p_sig q_sig
## 1 0.005212856 Low ** -1 tox (sev vs low) TRUE TRUE
## 2 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 3 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 4 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 5 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 6 0.364869699 Low † -1 tox (sev vs low) TRUE FALSE
## 7 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 8 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 9 0.078943954 Low -† -1 tox (sev vs low) TRUE FALSE
## 10 0.374002022 Low -1 tox (sev vs low) FALSE FALSE
## 11 0.119868301 Low † -1 tox (sev vs low) TRUE FALSE
## 12 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 13 0.564883266 Low -1 tox (sev vs low) FALSE FALSE
## 14 0.688193875 Low -1 tox (sev vs low) FALSE FALSE
## 15 0.070553444 Low -† -1 tox (sev vs low) TRUE FALSE
## 16 0.190357742 Low † -1 tox (sev vs low) TRUE FALSE
## 17 0.510256117 Low -1 tox (sev vs low) FALSE FALSE
## 18 0.452216725 Low -1 tox (sev vs low) FALSE FALSE
## 19 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 20 0.631199479 Low -1 tox (sev vs low) FALSE FALSE
## 21 0.564883266 Low -1 tox (sev vs low) FALSE FALSE
## 22 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 23 0.688193875 Low -1 tox (sev vs low) FALSE FALSE
## 24 0.374002022 Low -1 tox (sev vs low) FALSE FALSE
## 25 0.083192799 Low -† -1 tox (sev vs low) TRUE FALSE
## 26 0.521801591 Low -1 tox (sev vs low) FALSE FALSE
## 27 0.632480819 Low -1 tox (sev vs low) FALSE FALSE
## 28 0.706246917 Low -1 tox (sev vs low) FALSE FALSE
## 29 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 30 0.706246917 Low -1 tox (sev vs low) FALSE FALSE
## 31 0.646468622 Low -1 tox (sev vs low) FALSE FALSE
## 32 0.249064747 Low † -1 tox (sev vs low) TRUE FALSE
## 33 0.650979415 Low -1 tox (sev vs low) FALSE FALSE
## 34 0.650979415 Low -1 tox (sev vs low) FALSE FALSE
## 35 0.736828627 Low -1 tox (sev vs low) FALSE FALSE
## 36 0.153736297 Low † -1 tox (sev vs low) TRUE FALSE
## 37 0.689628285 Low -1 tox (sev vs low) FALSE FALSE
## 38 0.706246917 Low -1 tox (sev vs low) FALSE FALSE
## 39 0.761799579 Low -1 tox (sev vs low) FALSE FALSE
## 40 0.736828627 Low -1 tox (sev vs low) FALSE FALSE
## 41 0.715141757 Low -1 tox (sev vs low) FALSE FALSE
## 42 0.761799579 Low -1 tox (sev vs low) FALSE FALSE
## 43 0.249064747 Low † -1 tox (sev vs low) TRUE FALSE
## 44 0.715082426 Low -1 tox (sev vs low) FALSE FALSE
## 45 0.761799579 Low -1 tox (sev vs low) FALSE FALSE
## 46 0.761799579 Low -1 tox (sev vs low) FALSE FALSE
## 47 0.119868301 Low † -1 tox (sev vs low) TRUE FALSE
## 48 0.438174233 Low -1 tox (sev vs low) FALSE FALSE
## 49 0.831347896 Low -1 tox (sev vs low) FALSE FALSE
## 50 0.847204527 Low -1 tox (sev vs low) FALSE FALSE
## 51 0.772895134 Low -1 tox (sev vs low) FALSE FALSE
## 52 0.374002022 Low -1 tox (sev vs low) FALSE FALSE
## 53 0.738582142 Low -1 tox (sev vs low) FALSE FALSE
## 54 0.364869699 Low † -1 tox (sev vs low) TRUE FALSE
## 55 0.650979415 Low -1 tox (sev vs low) FALSE FALSE
## 56 0.313810715 Low † -1 tox (sev vs low) TRUE FALSE
## 57 0.598309770 Sev 1 tox (sev vs low) FALSE FALSE
## 58 0.786781671 Sev 1 tox (sev vs low) FALSE FALSE
## 59 0.736828627 Sev 1 tox (sev vs low) FALSE FALSE
## 60 0.761799579 Sev 1 tox (sev vs low) FALSE FALSE
## 61 0.689628285 Sev 1 tox (sev vs low) FALSE FALSE
## 62 0.786781671 Sev 1 tox (sev vs low) FALSE FALSE
## 63 0.786781671 Sev 1 tox (sev vs low) FALSE FALSE
## 64 0.761799579 Sev 1 tox (sev vs low) FALSE FALSE
## 65 0.689628285 Sev 1 tox (sev vs low) FALSE FALSE
## 66 0.761799579 Sev 1 tox (sev vs low) FALSE FALSE
## 67 0.715141757 Sev 1 tox (sev vs low) FALSE FALSE
## 68 0.706246917 Sev 1 tox (sev vs low) FALSE FALSE
## 69 0.521801591 Sev 1 tox (sev vs low) FALSE FALSE
## 70 0.313810715 Sev † 1 tox (sev vs low) TRUE FALSE
## 71 0.374002022 Sev 1 tox (sev vs low) FALSE FALSE
## 72 0.008280515 Sev ** 1 tox (sev vs low) TRUE TRUE
## sig alpha_grp Genus
## 1 BH<0.05 1 g__Parvimonas
## 2 p<0.05 (solo) 0.3 g__Fusobacterium
## 3 p<0.05 (solo) 0.3 g__Peptostreptococcus
## 4 p<0.05 (solo) 0.3 g__[Ruminococcus]_gnavus_group
## 5 p<0.05 (solo) 0.3 g__Solobacterium
## 6 p<0.05 (solo) 0.3 g__Barnesiella
## 7 p<0.05 (solo) 0.3 o__Oscillospirales_NA
## 8 p<0.05 (solo) 0.3 g__Porphyromonas
## 9 p<0.05 (solo) 0.3 g__uncultured
## 10 ns 0.3 g__Negativibacillus
## 11 p<0.05 (solo) 0.3 g__Izemoplasmatales
## 12 p<0.05 (solo) 0.3 g__Family_XIII_AD3011_group
## 13 ns 0.3 g__Senegalimassilia
## 14 ns 0.3 g__Butyrivibrio
## 15 p<0.05 (solo) 0.3 g__Hungatella
## 16 p<0.05 (solo) 0.3 g__Family_XIII_UCG-001
## 17 ns 0.3 g__Enterorhabdus
## 18 ns 0.3 g__Gemella
## 19 p<0.05 (solo) 0.3 g__[Eubacterium]_nodatum_group
## 20 ns 0.3 g__UCG-010
## 21 ns 0.3 g__UBA1819
## 22 p<0.05 (solo) 0.3 g__Intestinimonas
## 23 ns 0.3 g__Butyricimonas
## 24 ns 0.3 g__Coprobacillus
## 25 p<0.05 (solo) 0.3 g__[Ruminococcus]_torques_group
## 26 ns 0.3 g__Fournierella
## 27 ns 0.3 g__UCG-003
## 28 ns 0.3 g__Anaerostipes
## 29 p<0.05 (solo) 0.3 g__UCG-009
## 30 ns 0.3 g__Lachnoclostridium
## 31 ns 0.3 g__Lachnospiraceae_UCG-010
## 32 p<0.05 (solo) 0.3 g__Hydrogenoanaerobacterium
## 33 ns 0.3 g__[Eubacterium]_hallii_group
## 34 ns 0.3 g__Parabacteroides
## 35 ns 0.3 g__[Eubacterium]_ruminantium_group
## 36 p<0.05 (solo) 0.3 g__Merdibacter
## 37 ns 0.3 g__Oscillibacter
## 38 ns 0.3 g__Clostridium_sensu_stricto_1
## 39 ns 0.3 g__Howardella
## 40 ns 0.3 g__Bilophila
## 41 ns 0.3 g__uncultured
## 42 ns 0.3 g__Streptococcus
## 43 p<0.05 (solo) 0.3 g__[Eubacterium]_fissicatena_group
## 44 ns 0.3 g__Butyricicoccus
## 45 ns 0.3 g__uncultured
## 46 ns 0.3 g__Romboutsia
## 47 p<0.05 (solo) 0.3 g__Eubacterium
## 48 ns 0.3 g__[Clostridium]_innocuum_group
## 49 ns 0.3 g__Paraprevotella
## 50 ns 0.3 g__Escherichia-Shigella
## 51 ns 0.3 g__RF39
## 52 ns 0.3 f__Butyricicoccaceae_NA
## 53 ns 0.3 g__Collinsella
## 54 p<0.05 (solo) 0.3 g__Dielma
## 55 ns 0.3 g__uncultured
## 56 p<0.05 (solo) 0.3 g__Cloacibacillus
## 57 ns 0.3 g__Moryella
## 58 ns 0.3 g__[Eubacterium]_siraeum_group
## 59 ns 0.3 g__Olsenella
## 60 ns 0.3 g__Asteroleplasma
## 61 ns 0.3 g__Haemophilus
## 62 ns 0.3 g__Monoglobus
## 63 ns 0.3 g__Phascolarctobacterium
## 64 ns 0.3 g__Ruminococcus
## 65 ns 0.3 g__Faecalibacterium
## 66 ns 0.3 g__Bifidobacterium
## 67 ns 0.3 g__Roseburia
## 68 ns 0.3 g__Agathobacter
## 69 ns 0.3 g__Lachnospiraceae_NK4A136_group
## 70 p<0.05 (solo) 0.3 g__[Eubacterium]_ventriosum_group
## 71 ns 0.3 g__Lachnospira
## 72 BH<0.05 1 g__Fusicatenibacter
## label p_only sig_q
## 1 g__Parvimonas FALSE q<0.01
## 2 g__Fusobacterium TRUE q<0.10
## 3 g__Peptostreptococcus TRUE q<0.10
## 4 g__[Ruminococcus]_gnavus_group TRUE ns
## 5 g__Solobacterium TRUE q<0.10
## 6 g__Barnesiella TRUE ns
## 7 o__Oscillospirales_NA TRUE q<0.10
## 8 g__Porphyromonas TRUE q<0.10
## 9 g__uncultured TRUE q<0.10
## 10 g__Negativibacillus FALSE ns
## 11 g__Izemoplasmatales TRUE ns
## 12 g__Family_XIII_AD3011_group TRUE ns
## 13 g__Senegalimassilia FALSE ns
## 14 g__Butyrivibrio FALSE ns
## 15 g__Hungatella TRUE q<0.10
## 16 g__Family_XIII_UCG-001 TRUE ns
## 17 g__Enterorhabdus FALSE ns
## 18 g__Gemella FALSE ns
## 19 g__[Eubacterium]_nodatum_group TRUE ns
## 20 g__UCG-010 FALSE ns
## 21 g__UBA1819 FALSE ns
## 22 g__Intestinimonas TRUE ns
## 23 g__Butyricimonas FALSE ns
## 24 g__Coprobacillus FALSE ns
## 25 g__[Ruminococcus]_torques_group TRUE q<0.10
## 26 g__Fournierella FALSE ns
## 27 g__UCG-003 FALSE ns
## 28 g__Anaerostipes FALSE ns
## 29 g__UCG-009 TRUE ns
## 30 g__Lachnoclostridium FALSE ns
## 31 g__Lachnospiraceae_UCG-010 FALSE ns
## 32 g__Hydrogenoanaerobacterium TRUE ns
## 33 g__[Eubacterium]_hallii_group FALSE ns
## 34 g__Parabacteroides FALSE ns
## 35 g__[Eubacterium]_ruminantium_group FALSE ns
## 36 g__Merdibacter TRUE ns
## 37 g__Oscillibacter FALSE ns
## 38 g__Clostridium_sensu_stricto_1 FALSE ns
## 39 g__Howardella FALSE ns
## 40 g__Bilophila FALSE ns
## 41 g__uncultured FALSE ns
## 42 g__Streptococcus FALSE ns
## 43 g__[Eubacterium]_fissicatena_group TRUE ns
## 44 g__Butyricicoccus FALSE ns
## 45 g__uncultured FALSE ns
## 46 g__Romboutsia FALSE ns
## 47 g__Eubacterium TRUE ns
## 48 g__[Clostridium]_innocuum_group FALSE ns
## 49 g__Paraprevotella FALSE ns
## 50 g__Escherichia-Shigella FALSE ns
## 51 g__RF39 FALSE ns
## 52 f__Butyricicoccaceae_NA FALSE ns
## 53 g__Collinsella FALSE ns
## 54 g__Dielma TRUE ns
## 55 g__uncultured FALSE ns
## 56 g__Cloacibacillus TRUE ns
## 57 g__Moryella FALSE ns
## 58 g__[Eubacterium]_siraeum_group FALSE ns
## 59 g__Olsenella FALSE ns
## 60 g__Asteroleplasma FALSE ns
## 61 g__Haemophilus FALSE ns
## 62 g__Monoglobus FALSE ns
## 63 g__Phascolarctobacterium FALSE ns
## 64 g__Ruminococcus FALSE ns
## 65 g__Faecalibacterium FALSE ns
## 66 g__Bifidobacterium FALSE ns
## 67 g__Roseburia FALSE ns
## 68 g__Agathobacter FALSE ns
## 69 g__Lachnospiraceae_NK4A136_group FALSE ns
## 70 g__[Eubacterium]_ventriosum_group TRUE ns
## 71 g__Lachnospira FALSE ns
## 72 g__Fusicatenibacter FALSE q<0.01
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## Pseudo-count approach is used.
As LinDA shows a strict criteria, we are showing and retaining the features that shows at least qval < 0.1.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## No shared levels found between `names(values)` of the manual scale and the
## data's alpha values.
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 144 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 224 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 224 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
## [1] "matrix" "array"
## For proportion and other data types, posterior sampling will not be performed!
## The data has 36 samples and 224 features will be tested!
## On average, 1 outlier counts will be replaced for each feature!
## Permutation testing ...
## .........
## Completed!
This study integrates clinical stratification with microbiome profiling to answer the research questions from section Research Questions.
[R] Key Results
These results suggest that two principal bacteria could be set as bacterial biomarkers: Parvimonas for low toxicity classification; and Fusicatenibacter for severe toxicity classification.
Another bacterial genera could be useful to keep in future evaluations such as - Lachnospira and Lachnospiraceae NK4A136 - Family XIII AD3011 group - Ruminococcus torques and Ruminococcus gnavus - Eubacterium ventriosum could be of interest in severe toxicity. - Eubacterium genera members could help if the taxonomical level arrives to species or strain, in order to be splitted between the two toxicity levels evaluated. - This could help with Oscillospirales order members too.
Interestingly, Blautia species or strains could be relevant to separate between these two toxicities classes. That is one of the reasons why future work will be focus on MiniON Oxford Nanopore sequencing.
[M] Methodological Note
In the case of ALDEx2 and LEfSE, no features were retained when the BH correction was applied, independently that if this was of the unfiltered or filtered by prevalence. Additionally, ALDEx2 was one of the method that allowed the cofounders addition. Surprisedly, it didn’t return results when BH correction was applied.
ANCOM-BC reported the highest number of new biomarkers to be kept in mind for future studies. Meanwhile, ALDEx2 still did not return any significant biomarkers.
LinDA returned some relevant bacterial biomarkers previously related with colorectal cancer prognosis such as Bifidobacterium or Streptococcus.
-ZicoSeq and DESeq2 also kept similar results in number and in bacterial detection reported before the use of confounders.
When the initial analysis started with the difficulties this research faced (low dataset size), the use of confounders is a great strategy to get meaningful outputs. So, the next step will also evaluated this subsection of the report in more detail.
[*] Conclusion In conclusion, this study demonstrates the utility of integrating multiple DAA methodologies and normalisation strategies to identify microbial biomarkers associated with chemotherapy-related toxicity in colorectal cancer. Of note, compositionally aware methods such as ANCOM-BC, LinDA, and ZicoSeq proved most sensitive, detecting subtle microbial shifts in cohorts with minimal clinical divergence. DESeq2 reported a medium conservative and sensitive results, although only one feature was retained in the filtered, BH correction test. The more surprising tool was LEfSe, which reported no features after BH correction in both filtered and unfiltered approaches (even with two different normalizations: CPM vs TSS). Finally, ALDEx2 was the most conservative, reporting no features when FDR control was applied.
Despite differences in statistical assumptions and data transformations, the consistent detection of and across analytical frameworks and filtering approaches underscores their potential as key indicators of chemotherapy response. Also, other bacteria, such as the , , , and genera, could be considered for further study. Notably, the oral bacteria could be a validated CRC-associated biomarker, emerges as a promising new target for investigation.
However, future work should increase the sample size and explore other sequencing technologies, such as Oxford Nanopore (ONT). Applying complementary DAA methodologies or machine learning frameworks to validate these microbial signatures will also be essential to elucidate their roles.
[!] Limitation
A key limitation is the small sample size, which leads to sparse cells for some locations and reduces power, precision and generalisability. Accordingly, estimates should be interpreted cautiously, with emphasis on effect sizes and uncertainty intervals rather than sole reliance on p-values; results warrant external validation and sensitivity analyses across DAA pipelines. We position these findings within prior literature [@cite1; @cite2] and outline implications for patient stratification and future study design.
[>>] Future Work
This report is explained and discussed in the paper entitled: “Evaluating DAA methodologies to detect microbiome taxa associated with Chemotherapy toxicity in a CRC cohort”. Currently under submission.
NOTE (1): The dataset evaluated in this research was a subset of the dataset analysed in previous paper such as the one mentioned in NOTE (2) (below).
NOTE (2): The ancombc global function code was adapted from the papers:
Conde-Pérez et al. The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis. (2024). Molecular Oncology. DOI: https://doi.org/10.1002/1878-0261.13604.
Conde-Pérez, K., Buetas, E., Aja-Macaya, P., Martin-De Arribas, E., Iglesias-Corrás, I., Trigo-Tasende, N., Nasser-Ali, M., Estévez, L. S., Rumbo-Feal, S., Otero-Alén, B., Noguera, J. F., Concha, Á., Pardiñas-López, S., Carda-Diéguez, M., Gómez-Randulfe, I., Martínez-Lago, N., Ladra, S., Aparicio, L. A., Bou, G., . . . Poza, M. (2024). Parvimonas micra can translocate from the subgingival sulcus of the human oral cavity to colorectal adenocarcinoma. Molecular Oncology, 18(5), 1143-1173. DOI: https://doi.org/10.1002/1878-0261.13506.
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=es_ES.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=es_ES.UTF-8 LC_COLLATE=es_ES.UTF-8
## [5] LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=es_ES.UTF-8
## [7] LC_PAPER=es_ES.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Madrid
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggVennDiagram_1.5.4 rsample_1.3.1
## [3] tidygraph_1.3.1 ape_5.8-1
## [5] ggtree_3.16.0 scales_1.4.0
## [7] igraph_2.2.1 ggraph_2.2.1
## [9] viridis_0.6.5 viridisLite_0.4.2
## [11] tibble_3.3.0 pheatmap_1.0.13
## [13] GUniFrac_1.8 LinDA_0.2.0
## [15] DESeq2_1.48.1 SummarizedExperiment_1.38.1
## [17] Biobase_2.68.0 MatrixGenerics_1.20.0
## [19] matrixStats_1.5.0 GenomicRanges_1.60.0
## [21] GenomeInfoDb_1.44.3 IRanges_2.42.0
## [23] S4Vectors_0.46.0 BiocGenerics_0.54.1
## [25] generics_0.1.4 ALDEx2_1.40.0
## [27] latticeExtra_0.6-30 lattice_0.22-7
## [29] zCompositions_1.5.0-5 survival_3.8-3
## [31] truncnorm_1.0-9 MASS_7.3-65
## [33] ANCOMBC_2.10.1 microbiome_1.30.0
## [35] microbiomeMarker_1.13.2 vegan_2.7-2
## [37] permute_0.9-8 tidytree_0.4.6
## [39] phyloseq_1.52.0 glue_1.8.0
## [41] ggvenn_0.1.10 colorblindcheck_1.0.2
## [43] randomcoloR_1.1.0.1 plotly_4.11.0
## [45] camcorder_0.1.0 reprtree_0.6
## [47] plotrix_3.8-4 tree_1.0-44
## [49] randomForest_4.7-1.2 ggalluvial_0.12.5
## [51] egg_0.4.5 gridExtra_2.3
## [53] RColorBrewer_1.1-3 VennDiagram_1.7.3
## [55] futile.logger_1.4.3 readr_2.1.5
## [57] patchwork_1.3.1 ggh4x_0.3.1
## [59] gtools_3.9.5 stringr_1.5.2
## [61] janitor_2.2.1 ggrepel_0.9.6
## [63] ggplot2_4.0.0 dplyr_1.1.4
## [65] plyr_1.8.9 purrr_1.1.0
## [67] tidyr_1.3.1 qiime2R_0.99.6
## [69] formatR_1.14 reshape2_1.4.4
## [71] data.table_1.17.8 xtable_1.8-4
## [73] devtools_2.4.5 usethis_3.1.0
## [75] readxl_1.4.5 markdown_2.0
## [77] rmarkdown_2.30 kableExtra_1.4.0
## [79] knitr_1.50
##
## loaded via a namespace (and not attached):
## [1] coin_1.4-3 gld_2.6.7 urlchecker_1.0.1
## [4] nnet_7.3-20 DT_0.33 Biostrings_2.76.0
## [7] TH.data_1.1-3 vctrs_0.6.5 energy_1.7-12
## [10] digest_0.6.37 png_0.1-8 shape_1.4.6.1
## [13] proxy_0.4-27 Exact_3.3 parallelly_1.45.0
## [16] deldir_2.0-4 magick_2.8.7 httpuv_1.6.16
## [19] foreach_1.5.2 withr_3.0.2 xfun_0.53
## [22] ggfun_0.1.9 ellipsis_0.3.2 doRNG_1.8.6.2
## [25] memoise_2.0.1 profvis_0.4.0 gmp_0.7-5
## [28] systemfonts_1.3.1 ragg_1.4.0 zoo_1.8-14
## [31] GlobalOptions_0.1.2 V8_6.0.4 Formula_1.2-5
## [34] promises_1.4.0 otel_0.2.0 httr_1.4.7
## [37] globals_0.18.0 rhdf5filters_1.20.0 rhdf5_2.52.1
## [40] rstudioapi_0.17.1 UCSC.utils_1.4.0 miniUI_0.1.2
## [43] base64enc_0.1-3 curl_7.0.0 polyclip_1.10-7
## [46] statip_0.2.3 quadprog_1.5-8 GenomeInfoDbData_1.2.14
## [49] SparseArray_1.8.0 ade4_1.7-23 doParallel_1.0.17
## [52] evaluate_1.0.5 S4Arrays_1.8.1 gifski_1.32.0-2
## [55] Rfast_2.1.5.1 hms_1.1.3 glmnet_4.1-9
## [58] colorspace_2.1-2 magrittr_2.0.4 snakecase_0.11.1
## [61] modeltools_0.2-24 later_1.4.4 class_7.3-23
## [64] Hmisc_5.2-3 pillar_1.11.1 nlme_3.1-168
## [67] iterators_1.0.14 caTools_1.18.3 compiler_4.5.1
## [70] plotROC_2.3.1 stringi_1.8.7 biomformat_1.36.0
## [73] DescTools_0.99.60 stabledist_0.7-2 minqa_1.2.8
## [76] lubridate_1.9.4 crayon_1.5.3 abind_1.4-8
## [79] timeSeries_4041.111 gridGraphics_0.5-1 emdbook_1.3.13
## [82] locfit_1.5-9.12 haven_2.5.5 graphlayouts_1.2.2
## [85] bit_4.6.0 rootSolve_1.8.2.4 sandwich_3.1-1
## [88] libcoin_1.0-10 codetools_0.2-20 multcomp_1.4-28
## [91] textshaping_1.0.1 directlabels_2025.6.24 bslib_0.9.0
## [94] e1071_1.7-16 lmom_3.2 GetoptLong_1.0.5
## [97] multtest_2.64.0 mime_0.13 splines_4.5.1
## [100] metagenomeSeq_1.50.0 circlize_0.4.16 Rcpp_1.1.0
## [103] cellranger_1.1.0 interp_1.1-6 utf8_1.2.6
## [106] clue_0.3-66 apeglm_1.30.0 fBasics_4041.97
## [109] lme4_1.1-37 fs_1.6.6 listenv_0.9.1
## [112] checkmate_2.3.2 Rdpack_2.6.4 pkgbuild_1.4.8
## [115] expm_1.0-0 gsl_2.1-8 ggplotify_0.1.2
## [118] Matrix_1.7-4 statmod_1.5.1 tzdb_0.5.0
## [121] svglite_2.2.1 tweenr_2.0.3 pkgconfig_2.0.3
## [124] tools_4.5.1 cachem_1.1.0 rbibutils_2.3
## [127] numDeriv_2016.8-1.1 zigg_0.0.2 rmutil_1.1.10
## [130] fastmap_1.2.0 sass_0.4.10 coda_0.19-4.1
## [133] stable_1.1.6 rpart_4.1.24 farver_2.1.2
## [136] reformulas_0.4.1 mgcv_1.9-3 yaml_2.3.10
## [139] spatial_7.3-18 foreign_0.8-90 cli_3.6.5
## [142] lifecycle_1.0.4 mvtnorm_1.3-3 lambda.r_1.2.4
## [145] sessioninfo_1.2.3 backports_1.5.0 modeest_2.4.0
## [148] BiocParallel_1.42.1 timechange_0.3.0 gtable_0.3.6
## [151] rjson_0.2.23 parallel_4.5.1 limma_3.64.1
## [154] CVXR_1.0-15 jsonlite_2.0.0 bitops_1.0-9
## [157] bit64_4.6.0-1 Rtsne_0.17 yulab.utils_0.2.0
## [160] RcppParallel_5.1.10 bdsmatrix_1.3-7 futile.options_1.0.1
## [163] jquerylib_0.1.4 timeDate_4041.110 lazyeval_0.2.2
## [166] shiny_1.11.1 htmltools_0.5.8.1 Wrench_1.26.0
## [169] XVector_0.48.0 treeio_1.32.0 jpeg_0.1-11
## [172] boot_1.3-32 R6_2.6.1 gplots_3.2.0
## [175] labeling_0.4.3 Rmpfr_1.1-0 forcats_1.0.0
## [178] bbmle_1.0.25.1 cluster_2.1.8.1 rngtools_1.5.2
## [181] pkgload_1.4.0 Rhdf5lib_1.30.0 aplot_0.2.8
## [184] nloptr_2.2.1 DelayedArray_0.34.1 tidyselect_1.2.1
## [187] htmlTable_2.4.3 ggforce_0.5.0 inline_0.3.21
## [190] xml2_1.3.8 future_1.58.0 KernSmooth_2.23-26
## [193] S7_0.2.0 furrr_0.3.1 rsvg_2.6.2
## [196] htmlwidgets_1.6.4 ComplexHeatmap_2.24.1 rlang_1.1.6
## [199] lmerTest_3.1-3 remotes_2.5.0